This document summarizes a 2010 tutorial on metric learning given by Brian Kulis at the University of California, Berkeley. The tutorial introduces metric learning problems and algorithms. It discusses how metric learning can learn feature weights or linear/nonlinear transformations from data to improve distance metrics for tasks like clustering and classification. Key topics covered include Mahalanobis distance metrics, linear and nonlinear metric learning methods, and applications. The tutorial aims to explain both theoretical concepts and practical considerations for metric learning.
Predictive uncertainty of deep models and its applicationsNAVER Engineering
발표자: 이기민(KAIST 박사과정)
발표일: 2018.4.
The predictive uncertainty (e.g., entropy of softmax distribution of a deep classifier) is indispensable as it is useful in many machine learning applications (e.g., active learning and ensemble learning) as well as when deploying the trained model in real-world systems. In order to improve the quality of the predictive uncertainty, we proposed a novel loss function for training deep models (ICLR 2018). We showed that confidence deep models trained by our method can be very useful in various machine learning applications such as novelty detection (CVPR 2018) and ensemble learning (ICML 2017).
This presentation discusses multimodal deep learning and unsupervised feature learning from audio and video speech data. It introduces the McGurk effect where audio-visual speech is integrated. An autoencoder model is used to learn shared representations from audio and video input that outperform single modality learning on lip-reading tasks. On the AVLetters dataset, the cross-modality features achieved a classification accuracy of 64.4%, and on the CUAVE dataset, an accuracy of 68.7%.
2019 FIDO Tokyo Seminar - LINE PayへのFIDO2実装FIDO Alliance
This document summarizes LINE's deployment of FIDO2 authentication for its LINE Pay service. It discusses how passwords are insecure and the root of many breaches. FIDO2 provides a stronger alternative using public/private key attestation and is designed to be privacy-preserving. LINE joined the FIDO Alliance in 2017 and certified its universal server in 2018. It has implemented FIDO2 authentication flows for iOS using Touch ID/Face ID and for Android. Future plans include expanding FIDO2 to more financial services and LINE applications to encourage password-less authentication.
1. The document discusses variational inference and how dropout can be interpreted as a Bayesian approximation method. Dropout is shown to be equivalent to placing a variational distribution over the weights of a neural network.
2. Evaluating the evidence lower bound allows dropout to be framed as variational inference. However, this can underestimate model uncertainty. Alternative divergence measures like chi-square and inclusive KL divergences provide upper bounds that help address this issue.
3. Monte Carlo dropout can estimate epistemic uncertainty by approximating predictive distributions during testing. Overall, the document examines how dropout relates to Bayesian deep learning and variational inference through approximations of evidence bounds.
Distance metric learning is a technique to learn a distance metric from training data to improve the performance of algorithms like classification and clustering. Large Margin Nearest Neighbor (LMNN) is an approach that learns a Mahalanobis distance metric for k-nearest neighbor classification by formulating it as a semidefinite program to minimize a cost function. It aims to bring similar examples closer while pushing dissimilar examples farther apart with a margin of at least 1 unit. Large Margin Component Analysis (LMCA) extends LMNN to high dimensional data by directly optimizing the objective with respect to a non-square dimensionality reduction matrix rather than a square distance metric matrix.
This document describes the ORB (Oriented FAST and Rotated BRIEF) image feature detection and description algorithm. It first discusses image features in general, including corner detection methods like FAST, BRIEF and SIFT. It then provides an overview of ORB, which combines oFAST for keypoint detection with rBRIEF for descriptor extraction. The document proceeds to explain the details of the ORB algorithm, including using an image pyramid for scale invariance, computing keypoint orientations, and generating rotation-invariant BRIEF descriptors.
A Machine Learning Framework for Materials Knowledge Systemsaimsnist
- The document describes a machine learning framework for developing artificial intelligence-based materials knowledge systems (MKS) to support accelerated materials discovery and development.
- The MKS would have main functions of diagnosing materials problems, predicting materials behaviors, and recommending materials selections or process adjustments.
- It would utilize a Bayesian statistical approach to curate process-structure-property linkages for all materials classes and length scales, accounting for uncertainty in the knowledge, and allow continuous updates from new information sources.
This document summarizes a 2010 tutorial on metric learning given by Brian Kulis at the University of California, Berkeley. The tutorial introduces metric learning problems and algorithms. It discusses how metric learning can learn feature weights or linear/nonlinear transformations from data to improve distance metrics for tasks like clustering and classification. Key topics covered include Mahalanobis distance metrics, linear and nonlinear metric learning methods, and applications. The tutorial aims to explain both theoretical concepts and practical considerations for metric learning.
Predictive uncertainty of deep models and its applicationsNAVER Engineering
발표자: 이기민(KAIST 박사과정)
발표일: 2018.4.
The predictive uncertainty (e.g., entropy of softmax distribution of a deep classifier) is indispensable as it is useful in many machine learning applications (e.g., active learning and ensemble learning) as well as when deploying the trained model in real-world systems. In order to improve the quality of the predictive uncertainty, we proposed a novel loss function for training deep models (ICLR 2018). We showed that confidence deep models trained by our method can be very useful in various machine learning applications such as novelty detection (CVPR 2018) and ensemble learning (ICML 2017).
This presentation discusses multimodal deep learning and unsupervised feature learning from audio and video speech data. It introduces the McGurk effect where audio-visual speech is integrated. An autoencoder model is used to learn shared representations from audio and video input that outperform single modality learning on lip-reading tasks. On the AVLetters dataset, the cross-modality features achieved a classification accuracy of 64.4%, and on the CUAVE dataset, an accuracy of 68.7%.
2019 FIDO Tokyo Seminar - LINE PayへのFIDO2実装FIDO Alliance
This document summarizes LINE's deployment of FIDO2 authentication for its LINE Pay service. It discusses how passwords are insecure and the root of many breaches. FIDO2 provides a stronger alternative using public/private key attestation and is designed to be privacy-preserving. LINE joined the FIDO Alliance in 2017 and certified its universal server in 2018. It has implemented FIDO2 authentication flows for iOS using Touch ID/Face ID and for Android. Future plans include expanding FIDO2 to more financial services and LINE applications to encourage password-less authentication.
1. The document discusses variational inference and how dropout can be interpreted as a Bayesian approximation method. Dropout is shown to be equivalent to placing a variational distribution over the weights of a neural network.
2. Evaluating the evidence lower bound allows dropout to be framed as variational inference. However, this can underestimate model uncertainty. Alternative divergence measures like chi-square and inclusive KL divergences provide upper bounds that help address this issue.
3. Monte Carlo dropout can estimate epistemic uncertainty by approximating predictive distributions during testing. Overall, the document examines how dropout relates to Bayesian deep learning and variational inference through approximations of evidence bounds.
Distance metric learning is a technique to learn a distance metric from training data to improve the performance of algorithms like classification and clustering. Large Margin Nearest Neighbor (LMNN) is an approach that learns a Mahalanobis distance metric for k-nearest neighbor classification by formulating it as a semidefinite program to minimize a cost function. It aims to bring similar examples closer while pushing dissimilar examples farther apart with a margin of at least 1 unit. Large Margin Component Analysis (LMCA) extends LMNN to high dimensional data by directly optimizing the objective with respect to a non-square dimensionality reduction matrix rather than a square distance metric matrix.
This document describes the ORB (Oriented FAST and Rotated BRIEF) image feature detection and description algorithm. It first discusses image features in general, including corner detection methods like FAST, BRIEF and SIFT. It then provides an overview of ORB, which combines oFAST for keypoint detection with rBRIEF for descriptor extraction. The document proceeds to explain the details of the ORB algorithm, including using an image pyramid for scale invariance, computing keypoint orientations, and generating rotation-invariant BRIEF descriptors.
A Machine Learning Framework for Materials Knowledge Systemsaimsnist
- The document describes a machine learning framework for developing artificial intelligence-based materials knowledge systems (MKS) to support accelerated materials discovery and development.
- The MKS would have main functions of diagnosing materials problems, predicting materials behaviors, and recommending materials selections or process adjustments.
- It would utilize a Bayesian statistical approach to curate process-structure-property linkages for all materials classes and length scales, accounting for uncertainty in the knowledge, and allow continuous updates from new information sources.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document provides career advice for getting into the tech field, including:
- Doing projects and internships in college to build a portfolio.
- Learning about different roles and technologies through industry research.
- Contributing to open source projects to build experience and network.
- Developing a personal brand through a website and social media presence.
- Networking through events, communities, and finding a mentor.
- Practicing interviews through mock interviews and whiteboarding coding questions.
Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray
1. Core updates from Google periodically change how its algorithms assess and rank websites and pages. This can impact rankings through shifts in user intent, site quality issues being caught up to, world events influencing queries, and overhauls to search like the E-A-T framework.
2. There are many possible user intents beyond just transactional, navigational and informational. Identifying intent shifts is important during core updates. Sites may need to optimize for new intents through different content types and sections.
3. Responding effectively to core updates requires analyzing "before and after" data to understand changes, identifying new intents or page types, and ensuring content matches appropriate intents across video, images, knowledge graphs and more.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Time Management & Productivity - Best PracticesVit Horky
Here's my presentation on by proven best practices how to manage your work time effectively and how to improve your productivity. It includes practical tips and how to use tools such as Slack, Google Apps, Hubspot, Google Calendar, Gmail and others.
The six step guide to practical project managementMindGenius
The six step guide to practical project management
If you think managing projects is too difficult, think again.
We’ve stripped back project management processes to the
basics – to make it quicker and easier, without sacrificing
the vital ingredients for success.
“If you’re looking for some real-world guidance, then The Six Step Guide to Practical Project Management will help.”
Dr Andrew Makar, Tactical Project Management
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document provides career advice for getting into the tech field, including:
- Doing projects and internships in college to build a portfolio.
- Learning about different roles and technologies through industry research.
- Contributing to open source projects to build experience and network.
- Developing a personal brand through a website and social media presence.
- Networking through events, communities, and finding a mentor.
- Practicing interviews through mock interviews and whiteboarding coding questions.
Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray
1. Core updates from Google periodically change how its algorithms assess and rank websites and pages. This can impact rankings through shifts in user intent, site quality issues being caught up to, world events influencing queries, and overhauls to search like the E-A-T framework.
2. There are many possible user intents beyond just transactional, navigational and informational. Identifying intent shifts is important during core updates. Sites may need to optimize for new intents through different content types and sections.
3. Responding effectively to core updates requires analyzing "before and after" data to understand changes, identifying new intents or page types, and ensuring content matches appropriate intents across video, images, knowledge graphs and more.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
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Time Management & Productivity - Best PracticesVit Horky
Here's my presentation on by proven best practices how to manage your work time effectively and how to improve your productivity. It includes practical tips and how to use tools such as Slack, Google Apps, Hubspot, Google Calendar, Gmail and others.
The six step guide to practical project managementMindGenius
The six step guide to practical project management
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2. Outline
Introduction
Supervised Global Distance Metric Learning
Supervised Local Distance Metric Learning
Unsupervised Distance Metric Learning
Distance Metric Learning based on SVM
Kernel Methods for Distance Metrics Learning
Conclusions
3. Introduction
Definition
Distance Metric learning is to learn a distance metric for the
input space of data from a given collection of pair of
similar/dissimilar points that preserves the distance relation
among the training data pairs.
Importance
Many machine learning algorithms, heavily rely on the
distance metric for the input data patterns. e.g. kNN
A learned metric can significantly improve the performance
in classification, clustering and retrieval tasks:
e.g. KNN classifier, spectral clustering, content-based
image retrieval (CBIR).
4. Contributions of this Survey
Review distance metric learning under different learning
conditions
supervised learning vs. unsupervised learning
learning in a global sense vs. in a local sense
distance matrix based on linear kernel vs. nonlinear
kernel
Discuss central techniques of distance metric learning
K nearest neighbor
dimension reduction
semidefinite programming
kernel learning
large margin classification
5. Supervised
Distance Metric Learning
Local
Local Adaptive Distance
Metric Learning
Neighborhood Components Analysis
Relevant Component Analysis
Unsupervised
Distance Metric Learning Nonlinear embedding
LLE, ISOMAP, Laplacian Eigenmaps
Distance Metric Learning
based on SVM
Large Margin Nearest Neighbor
Based Distance Metric Learning
Cast Kernel Margin
Maximization into a SDP problem
Kernel Methods for
Distance Metrics Learning
Kernel Alignment with SDP
Learning with Idealized Kernel
Linear embedding
PCA, MDS
Global Distance Metric Learning
by Convex Programming
6. Outline
Introduction
Supervised Global Distance Metric Learning
Supervised Local Distance Metric Learning
Unsupervised Distance Metric Learning
Distance Metric Learning based on SVM
Kernel Methods for Distance Metrics Learning
7. Supervised Global Distance Metric
Learning (Xing et al. 2003)
Goal : keep all the data points within the same classes close,
while separating all the data points from different classes.
Formulate as a constrained convex programming problem
minimize the distance between the data pairs in S
Subject to data pairs in D are well separated
2
2
A
Equivalence constraints: {( , ) | and belong to the same class}
Inequivalence constraints: {( , ) | and belong to different classes},
d ( , ) ( ) ( ), is the distanc
i j i j
i j i j
T m m
A
S x x x x
D x x x x
x y x y x y A x y A S
e metric
8. Global Distance Metric Learning (Cont’d)
A is positive semi-definite
Ensure the negativity and the triangle inequality of the metric
The number of parameters is quadratic in the number of features
Difficult to scale to a large number of features
Simplify the computation
2 2
( , ) ( , )
min . . 0, 1
m m
i j i j
i j i j
A R
x x S x x D
A A
x x s t A x x
9. (a) Data Dist. of the original dataset (b) Data scaled by the global metric
Global Distance Metric Learning:
Example I
Keep all the data points within the same classes close
Separate all the data points from different classes
10. Global Distance Metric Learning:
Example II
Diagonalize distance metric A can simplify computation, but
could lead to disastrous results
(a) Original data (c) Rescaling by learned
diagonal A
(b) rescaling by learned
full A
11. (a) Data Dist. of the original dataset
Multimodal data distributions prevent global distance metrics
from simultaneously satisfying constraints on within-class
compactness and between-class separability.
(b) Data scaled by the global metric
Problems with Global Distance
Metric Learning
12. Outline
Introduction
Supervised Global Distance Metric Learning
Supervised Local Distance Metric Learning
Unsupervised Distance Metric Learning
Distance Metric Learning based on SVM
Kernel Methods for Distance Metrics Learning
Conclusions
13. Supervised Local Distance Metric
Learning
Local Adaptive Distance Metric Learning
Local Feature Relevance
Locally Adaptive Feature Relevance Analysis
Local Linear Discriminative Analysis
Neighborhood Components Analysis
Relevant Component Analysis
14. Local Adaptive Distance Metric
Learning
K Nearest Neighbor Classifier
0
0 0
1 1
0
( )
0
( ) : nearest neighbors of
, , , , :training examples
1
( )
0 . .
1
Pr( | ) ( )
( ) i
n n
i
i
i
x N x
N x x
x y x y
y j
y j
o w
j x y j
N x
15. Modified local neighborhood by a distance metric
Elongate the distance along the dimensions where
the class labels change rapidly
Squeeze the distance along the dimensions that are
almost independent from the class labels
Assumption of KNN
Pr(y|x) in the local NN is constant or smooth
However, this is not necessarily true!
Near class boundaries
Irrelevant dimensions
Local Adaptive Distance Metric
Learning
16. Local Feature Relevance
[J. Friedman,1994]
(x)p(x)dx
Ef f
i
[ | ] (x)p(x|x =z)dx,
i
E f x z f
i
p(x) ( )
p(x|x =z) =
p(x') ( ' )
i
i
x z
x z
2 2 2 2
( ) [( (x)-E ) | ] [( (x)-E( (x)| ) | ] ( [ | ])
i i i i i
I z E f f x z E f f x z x z Ef E f x z
2 2 2
1
( ) ( )/ ( )
p
i i i k k
k
r z I z I z
1
( , , )
m
z z z
i
x z
i
x z
x = z
Assume least-squared estimate for predicting f(x) is
Conditioned at , then the least-squared estimate of f(x)
The improvement in prediction error with knowing
Consider , a measure of relative influence of the
ith input variable to the variation of f(x) at is given by
17. Locally Adaptive Feature Relevance
Analysis [C. Domeniconi, 2002]
2
0
0
1 0
[ ( | X) ( | x )]
(X, x )
( | x )
J
j
p j p j
r
p j
0
x
Use a Chi-squared distance analysis to compute metric for
producing a neighborhood, in which
The posterior probabilities are approximately constant
Highly adaptive to query locations
Chi-squared distance between the true and estimated posterior
at the test point
Use the Chi-squared distance for feature relevance:
---- to tell to which extent the ith dimension can be relied on for
predicting p(j| )
0
x
18. Local Relevance Measure
in ith Dimension
2
i
1
[Pr( | ) Pr( | )]
r (z) =
Pr( | )
J
i i
j
i i
j z j x z
j x z
i
r (z)
Pr( | )
i i
j x z
is a conditional expectation of p(j|x)
Pr( | ) (Pr(j|x) | )
i i i i
j x z E x z
i
r (z) 0
x
The closer is to p(j|z), the more information the ith
dimension provides for predicting p(j|z)
measures the distance between Pr(j|z) and the conditional
expectation of Pr(j|x) at location z
Calculate for each point z in the neighborhood of
19. 0
i 0 0 1 0 0
0
1
( )
w ( ) ,where ( ) (max ( )) ( )
( )
t= 1 or 2, corresponds to linear and quadratic weighting.
t
q
i
i j j i
q
t
l
l
R x
x R x r x r x
R x
q
2
i=1
(x,y) = ( )
i i i
D w x y
Locally Adaptive Feature
Relevance Analysis
0
0
(x )
1
(x ) ( )
i i
z N
r r z
K
0
(x )
N is the neighborhood of 0
x
A local relevance measure in dimension i
Relative relevance
Weighted distance
20. Local Linear Discriminative Analysis
[T. Hastie et al. 1996]
Sb : the between-class covariance matrix
Sw : the within-class covariance matrix
-1
T = Sw Sb.
LDA finds principle eigenvectors of matrix
to keep patterns from the same class close
separate patterns from different classes apart
LDA metric : stacking principle eigenvectors of T together
21. Local Linear
Discriminative Analysis
1 1 1 1
2 2 2 2
[ I]
w w b w w
S S S S S
0
x
0
x
Need local adaptation of the nearest neighbor metric
Initialize as identical matrix
Given a testing point , iterate below two steps:
Estimate Sb and Sw based on the local neighbor
of measured by
Form a local metric behaving like LDA metric
is a small tuning parameter to prevent neighborhoods
extending to infinity
22. Local Sb shows the inconsistency of the class centriods
The estimated metric
shrinks the neighborhood in directions in which the local class
centroids differ to produce a neighborhood in which the class
centriod coincide
shrinks neighborhoods in directions orthogonal to these local
decision boundaries, and elongates them parallel to the boundaries.
Local Linear Discriminative Analysis
23. Overfitting, Scalability problem, # parameters is quadratic in #features.
Neighborhood Components Analysis
[J. Goldberger et al. 2005]
i
x
2
i j
i 2
i k
exp( Ax Ax )
Here C { | },
exp( Ax Ax )
i j ij
k i
j c c p
n
i
i=1
f(A) = p ,
i
p
i
ij
j C
p
NCA learns a Mahalanobis distance metric for the KNN
classifier by maximizing the leave-one-out cross validation.
The probability of classifying correctly,
weighted counting involving pairwise distance
The expected number of correctly classification points:
24. RCA [N. Shen et al. 2002]
unlabeled data labeled data
chuklet data
^ ^ ^
T
j j
ji ji
1 1
1
C (x m )(x m ) ,
j
n
k
j i
p
1
^ 2
y C x
j
^
n
j
ji i=1
chunklet j : {x } ,with mean m
Constructs a Mahalanobis distance metric based on a sum of
in-chunklet covariance matrices
Chunklet : data have same but unknown class labels
Sum of in-chunklet covariance matrices for p points in k chunklets:
Apply linear transformation
25. Information maximization
under chunklet constraints
[A. Bar-Hillel etal, 2003]
Maximizes the mutual information I(X,Y)
Constraints: within-chunklet compactness
T
2
j
B
1 1 B
Let B =A A, (*) can be further written into
1
max | B| s.t. m , B 0
p
j
n
k
ji
j i
x K
2
y
j
1 1
y
j
1
max I(X,Y) s.t. m . (*)
p
m is the transformed mean in the jth chunklet.
K is threshold constant.
j
n
k
ji
f F
j i
y K
26. RCA algorithm applied to
synthetic Gaussian data
(a) The fully labeled data set with 3 classes.
(b) Same data unlabeled; classes' structure is less evident.
(c) The set of chunklets
(d) The centered chunklets, and their empirical covariance.
(e) The RCA transformation applied to the chunklets. (centered)
(f) The original data after applying the RCA transformation.
27. Outline
Introduction
Supervised Global Distance Metric Learning
Supervised Local Distance Metric Learning
Unsupervised Distance Metric Learning
Distance Metric Learning based on SVM
Kernel Methods for Distance Metrics Learning
Conclusions
28. Unsupervised Distance Metric Learning
A Unified Framework for Dimension Reduction
Solution 1
Solution 2
linear nonlinear
Global PCA, MDS ISOMAP
Local LLE, Laplacian Eigenmap
Most dimension reduction approaches are to learn a distance
metric without label information. e.g. PCA
I will present five methods for dimensionality reduction.
29. Dimensionality Reduction Algorithms
PCA finds the subspace that best preserves the variance of the data.
MDS finds the subspace that best preserves the interpoint distances.
Isomap finds the subspace that best preserves the geodesic
interpoint distances. [Tenenbaum et al, 2000].
LLE finds the subspace that best preserves the local linear structure
of the data [Roweis and Saul, 2000].
Laplacian Eigenmap finds the subspace that best preserves local
neighborhood information in the adjacency graph [M. Belkin and P.
Niyogi,2003].
30. Multidimensional Scaling (MDS)
MDS finds the rank m projection that best preserves the
inter-point distance given by matrix D
Converts distances to inner products
Calculate X
Rank m projections Y closet to X
Given the distance matrix among
cities, MDS produces this map:
1
MDS MDS 2
m m
Y= V ( )
T
B= (D)= X X
1
MDS MDS 2
MDS MDS
[V , ] =eig(B)
X = V ( )
31. PCA (Principal Component Analysis)
1
PCA MDS PCA MDS PCA PCA MDS
2
V XV , , Y ( ) Y
=Var(X)
PCA PCA
[V , ]=eig( )
PCA
Y = V X
m
PCA finds the subspace that best preserves the data variance.
PCA projection of X with rank m
PCA vs. MDS
In the Euclidean case, MDS only differs from PCA by
starting with D and calculating X.
32. A B
Isometric Feature Mapping (ISOMAP)
[Tenenbaum et al, 2000]
Geodesic :the shortest curve on a manifold
that connects two points on the manifold
e.g. on a sphere, geodesics are great circles
Geodesic distance: length of the geodesic
Points far apart measured by geodesic dist.
appear close measured by Euclidean dist.
33. ISOMAP
Take a distance matrix as input
Construct a weighted graph G based on neighborhood relations
Estimate pairwise geodesic distance by
“a sequence of short hops” on G
Apply MDS to the geodesic distance matrix
34. Locally Linear Embedding (LLE)
[Roweis and Saul, 2000]
LLE finds the subspace that best preserves the local
linear structure of the data
Assumption: manifold is locally “linear”
Each sample in the input space is a linearly weighted
average of its neighbors.
A good projection should best preserve this geometric
locality property
35. LLE
W: a linear representation of every data point by its neighbors
Choose W by minimized the reconstruction error
Calculate a neighborhood preserving mapping Y, by minimizing
the reconstruction error
Y is given by the eigenvectors of the m lowest nonzero
eigenvalues of matrix
2
n
i ij
i=1 1
ij i ij j i
1
minimizing x W
s.t. W 1, x ; W 0 if x is not a neighbor of x
K
ij
j
n
j
x
* *
i
W
1
(Y)= y , where W argmin (W)
K
ij ij
i
W y
T
(I-W) (I-W)
36. Laplacian Eigenmap finds the subspace that best preserves local
neighborhood information in adjacency graph
Graph Laplacian: Given a graph G with weight matrix W
D is a diagonal matrix with
L =D –W is the graph Laplacian
Detailed steps:
Construct adjacency graph G.
Weight the edges:
Generalized eigen-decomposition of
Embedding : eigenvectors with top m nonzero eigenvalues
Laplacian Eigenmap
[M. Belkin and P. Niyogi,2003]
ii ji
j
D W
Lf= Df
ij
W 1, if nodes i and j are connected, and 0 otw.
37. A Unified Framework for
Dimension Reduction Algorithms
All use an eigendecomposition to obtain a lower-dimensional
embedding of data lying on a non-linear manifold.
Normalize affinity matrix
The embedding of has two alternative solutions
Solution 1 : (MDS & Isomap)
is the best approximation of in the squared error sense.
Solution 2 : (LLE & Laplacian Eigenmap)
i it ti
y with y = v
i it t it
e with e = v
^
t t
(H) the m largest positive eigenvalues and eigenvectors v
eig
i
x
i j
e ,e
^
Hij
ij
j
H ^
H H
38. Outline
Introduction
Supervised Global Distance Metric Learning
Supervised Local Distance Metric Learning
Unsupervised Distance Metric Learning
Distance Metric Learning based on SVM
Kernel Methods for Distance Metrics Learning
Conclusions
39. Distance Metric Learning based on SVM
Large Margin Nearest Neighbor Based Distance Metric
Learning
Objective Function
Reformulation as SDP
Cast Kernel Margin Maximization into a SDP Problem
Maximum Margin
Cast into SDP problem
Apply to Hard Margin and Soft Margin
40. After training
k=3 target neighbors lie within a smaller radius
differently labeled inputs lie outside this smaller radius with a
margin of at least one unit distance.
Large Margin Nearest Neighbor
Based Distance Metric Learning
[K. Weinberger et al., 2006]
Learns a Mahanalobis distance metric in the kNN classification
setting by SDP, that
Enforces the k-nearest neighbors belong to the same class
examples from different classes are separated by a large margin
41. Large Margin Nearest Neighbor
Based Distance Metric Learning
Cost function:
Penalize large distances between each input and its target neighbors
The hinge loss is incurred by differently labeled inputs whose
distances do not exceed the distance from input to any of its target
neighbors by one absolute unit of distance
-> do not threaten to invade each other’s neighborhoods
2 2 2
ij i j ij i j i l 2
2 2
ij ijl
(L) = L(x -x ) C (1 )[1 L(x -x ) L(x -x ) ]
[ ] max(z,0) denotes the standard hinge loss and the constant C > 0.
il
y
z
ij i j
ij j i
y {0,1} indicate whether or not the label y and y match
{0,1} indicate whether x is a target neighbor of x
i
x
42. Reformulation as SDP
T
i j i j
M
T T
i j i j i l i l
The resulting SDP is :
min (x x ) M(x x ) C (1 )
. . (x x ) M(x x ) (x x ) M(x x ) 1
0,M =0
ij ij il ijl
ij ijl
ijl
ijl
y
s t
2
i j i j i j
2
Let L(x -x ) (x -x ) M(x -x ), and introducing slack variable
T
ijl
43. Cast Kernel Margin Maximization
into a SDP Problem
[G. R. G. Lanckriet et al, 2004]
Maximum margin : the decision boundary has the maximum
minimum distance from the closest training point.
Hard Margin: linearly separable
Soft Margin: nonlinearly separable
The performance measure, generalized from dual solution of
different maximizing margin problem
T T T
, (K) max 2 ( ( ) ) : 0, y 0
with 0 on the training data w.r.t K. G is Gram matrix.
C
w e G K I C
44. Cast into SDP Problem
2 tr 2 tr , tr
K =0
min (K ) s.t. trace(K)=c. Here (K ) =w (K )
S S
w w
Hard Margin
1-norm soft margin
2-norm soft margin
tr tr ,0 tr
K =0
min (K ) s.t. trace(K)=c. Here (K ) =w (K )
w w
1 tr 1 tr C,0 tr
K =0
min (K ) s.t. trace(K)=c. Here (K ) =w (K )
S S
w w
K,t, , ,
tr
T T
min
. . trace(K)=c, K =0, 0, 0,
G(K ) y
0
( y) t-2C
tr
n
t
s t
I e
e e
,
K =0
min (K) . . trace(K) = c
C
w s t
45. Outline
Introduction
Supervised Global Distance Metric Learning
Supervised Local Distance Metric Learning
Unsupervised Distance Metric Learning
Distance Metric Learning based on SVM
Kernel Methods for Distance Metrics Learning
Conclusions
46. Kernel Methods for
Distance Metrics Learning
Learning a good kernel is equivalent to distance metric
learning
Kernel Alignment
Kernel Alignment with SDP
Learning with Idealized Kernel
Ideal Kernel
The Idealized Kernel
47. Kernel Alignment
[N. Cristianini,2001]
T
^ 1 F
1 2
1 1 F
K , yy
A(S, k ,k ) , y { 1}
K ,K
m
m
A measure of similarity between two kernel functions or between
a kernel and a target function
The inner product between two kernel matrices based on kernel k1
and k2.
The alignment of K1 and K2 w.r.t S:
Measure the degree of agreement between a kernel and a given
learning task.
1 2 1 i j 2 i j
F
, 1
K ,K K (x ,x )K (x ,x )
n
i j
^
1 2 F
1 2
1 1 2 2
F F
K ,K
A(S, k ,k )
K ,K K ,K
48. Kernel Alignment with SDP
[G. R. G. Lanckriet et al, 2004]
Optimizing the alignment between a set of labels and a
kernel matrix using SDP in a transductive setting.
Optimizing an objective function over the training data
block -> automatic tuning of testing data block
Introduce A with , this reduces to
T
tr
F
A,K
T
n
max K , yy
A K
. . trace(A) 1, K =0, =0.
K I
s t
^
T
1
K
max A( ,K ,yy ) s.t. K =0, trace(K) =1
S
tr tr,t
ij i j tr t
T
tr,t
K K
K= , where K (x ), (x ) ,i, j =1, ,n n .
K K
T
K K =A and trace(A) 1
49. Learning with Idealized Kernel
[J. T. Kwok and I.W. Tsang,2003]
Idealize a given kernel by making it more similar to the
ideal kernel matrix.
Ideal kernel:
Idealized kernel:
The alignment of will be greater than k, if
are the number of positive and negative samples.
Under the original distance metric M:
i j
*
i j
i j
1, y(x ) y(x )
k (x ,x )
0, y(x ) y(x )
~
*
k = k + k
2
*
2 2
K,K
n n
~
k
2
~ ~ ~ ij i j
2
ij i j
d y =y
K K 2K
d y y
ii jj ij
T 2 T
i j i j ij i j i j
k(x , x ) = x Mx , M =0; d (x - x ) M(x - x )
,
n n
50. ij
i j i j
2 T
S
2
B, ,
(x ,x ) (x ,x )
~ ~
2 2
ij ij i j
~
2 2
ij ij i j
1 1
min B , where B= AA
2
, (x ,x )
. . , 0, 0,
, (x ,x )
ij D ij
S D
S D
ij
ij
ij
C
C
N N
d d D
s t
d d S
Idealized kernel
We modify
Search for a matrix A under which
different classes : pulled apart by an amount of at least
same class :getting close together.
Introduce slack variables for error tolerance
2
~
ij i j
2
ij 2
ij i j
d y = y
d
d y y
2
~
T T
ij i j i j
(x - x ) A A(x - x )
d
51. Conclusions
A comprehensive review, covers:
Supervised distance metric learning
Unsupervised distance metric learning
Maximum margin based distance metric learning
approaches
Kernel methods towards distance metrics
Challenge:
Unsupervised distance metric learning.
Going local in a principle manner.
Learn an explicit nonlinear distance metric in the local
sense.
Efficiency issue.