In statistics, machine learning, and information theory, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
Process of converting data set having vast dimensions into data set with lesser dimensions ensuring that it conveys similar information concisely.
Concept
R code
In statistics, machine learning, and information theory, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
Process of converting data set having vast dimensions into data set with lesser dimensions ensuring that it conveys similar information concisely.
Concept
R code
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Principal Component Analysis (PCA) and LDA PPT SlidesAbhishekKumar4995
Machine learning (ML) technique use for Dimension reduction, feature extraction and analyzing huge amount of data are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are easily and interactively explained with scatter plot graph , 2D and 3D projection of Principal components(PCs) for better understanding.
Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies.
Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.
In this webinar, we will discuss :
How machine learning can help in identifying anomalies
Steps to approach an anomaly detection problem
Various techniques available for anomaly detection
Best algorithms that fit in different situations
Implementing an anomaly detection use case on the StreamAnalytix platform
To view the webinar - https://bit.ly/2IV2ahC
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data
A review of the paper “Ad Click Prediction: a View from the Trenches”
The paper discusses predicting ad click--through rates (CTR) which is a massive-scale learning problem central to the multi-billion dollar online advertising industry.
Presented by Mazen & Arzam in the Data Intensive Computing class at KTH, Stockholm, Sweden.
Link of the paper: http://research.google.com/pubs/pub41159.html
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Principal Component Analysis (PCA) and LDA PPT SlidesAbhishekKumar4995
Machine learning (ML) technique use for Dimension reduction, feature extraction and analyzing huge amount of data are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are easily and interactively explained with scatter plot graph , 2D and 3D projection of Principal components(PCs) for better understanding.
Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies.
Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.
In this webinar, we will discuss :
How machine learning can help in identifying anomalies
Steps to approach an anomaly detection problem
Various techniques available for anomaly detection
Best algorithms that fit in different situations
Implementing an anomaly detection use case on the StreamAnalytix platform
To view the webinar - https://bit.ly/2IV2ahC
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning.
Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data
A review of the paper “Ad Click Prediction: a View from the Trenches”
The paper discusses predicting ad click--through rates (CTR) which is a massive-scale learning problem central to the multi-billion dollar online advertising industry.
Presented by Mazen & Arzam in the Data Intensive Computing class at KTH, Stockholm, Sweden.
Link of the paper: http://research.google.com/pubs/pub41159.html
It's a well-known fact that the best explanation of a simple model is the model itself. But often we use complex models, such as ensemble methods or deep networks, so we cannot use the original model as its own best explanation because it is not easy to understand.
In the context of this topic, we will discuss how methods for interpreting model predictions work and will try to understand practical value of these methods.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Semantic Segmentation on Satellite ImageryRAHUL BHOJWANI
This is an Image Semantic Segmentation project targeted on Satellite Imagery. The goal was to detect the pixel-wise segmentation map for various objects in Satellite Imagery including buildings, water bodies, roads etc. The data for this was taken from the Kaggle competition <https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection>.
We implemented FCN, U-Net and Segnet Deep learning architectures for this task.
We analysed fertility rate on total population of Island which includes Northern Ireland and Republic of Ireland. We used “All Island Population dataset” and checked the relationship between the dependent variable and multiple independent variables to find the meaningful information to enhance sales. Tools: Python Programming.
Machine learning and linear regression programmingSoumya Mukherjee
Overview of AI and ML
Terminology awareness
Applications in real world
Use cases within Nokia
Types of Learning
Regression
Classification
Clustering
Linear Regression Single Variable with python
Valencian Summer School in Machine Learning 2017 - Day 1
Lectures Review: Summary Day 1 Sessions. By Mercè Martín (BigML).
https://bigml.com/events/valencian-summer-school-in-machine-learning-2017
By popular demand, here is a case study of my first Kaggle competition from about a year ago. Hope you find it useful. Thank you again to my fantastic team.
How to transform and select variables/features when creating a predictive model using machine learning. To see the source code visit https://github.com/Davisy/Feature-Engineering-and-Feature-Selection
A comparative review of various approaches for feature extraction in Face rec...Vishnupriya T H
Four feature extraction algorithms are discussed here.
1. Principal Component Analysis
2. Discreet LInear Transform
3. Independent Component Analysis
4. Linear Discriminant Aalysis
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
2. Agenda
● Curse Of Dimensionality
● Why Reduce Dimensions
● Types of Dimensionality Reduction
○ Feature Selection
○ Feature Extraction
● Application Specific Methods
2
3. Curse Of Dimensionality
3
The collection of issues that arise when dealing
with high dimensional data.
More data is good.
More detailed data (dimensions) might not be.
Curse Of Dimensionality
Why Reduce Dimensions
Types of Dimensionality
Reduction
● Feature Selection
● Feature Extraction
Application Specific
Methods
5. Just one more feature
Adding just one more feature could increase the amount of data
you need by an additional power.
In the age of Big Data, we still do not have enough data to
account for the curse of dimensionality.
5
6. Data Examples
● HD Images
○ Should we represent all pixels? Are they all useful?
● Video
○ What changes is what’s useful
● Text
○ Keywords vs all words
● Time series
○ Does every second matter?
6
7. Why Reduce Dimensions
7
1. Computation
Less dimensions allow us to compute models
more efficiently.
2. Visualization
Difficult to visualize more than 3 dimensions.
3. Remove Useless Information
Curse Of Dimensionality
Why Reduce Dimensions
Types of Dimensionality
Reduction
● Feature Selection
● Feature Extraction
Application Specific
Methods
9. Types of Dimensionality Reduction
9
● Feature Selection
Select features from the available features
● Feature Extraction
Generate synthetic features that represent the
available features.
Curse Of Dimensionality
Why Reduce Dimensions
Types of Dimensionality
Reduction
● Feature Selection
● Feature Extraction
Application Specific
Methods
10. Feature Selection
10
Select some features from the originally available
set.
1. Filter
2. Wrapper
3. Embedded
Curse Of Dimensionality
Why Reduce Dimensions
Types of Dimensionality
Reduction
● Feature Selection
● Feature Extraction
Application Specific
Methods
11. Process
1. Start with all features
2. Select a subset
3. Learn on the subset (train model)
4. Measure performance
11
15. Comparison
1. Filter
a. Pros: Low computation time and robust to overfitting
b. Cons: Select redundant data, results not as great, greedy
c. Used in pre-processing
2. Wrapper
a. Pros: Take learnings into consideration so better fit the data
b. Cons: Potentially high computation time, prone to overfitting, greedy
3. Embedded
a. Pros: Combine advantages of both Filter and Wrapper
b. Cons: Computation time
15
16. Example Methods
1. Filter
a. Mutual Information
2. Wrapper
a. Recursive Feature Elimination
3. Embedded
a. LASSO
b. LARS
16
18. Recursive Feature Elimination
Select features by recursively considering smaller and smaller sets
of features.
1. Train estimator and obtain importance of each feature
2. Prune least important k features
3. Repeat until only left with n features
Parameters: n features to keep, k features to drop per iteration
18
19. Norms Review
l1
norm: Manhattan distance or absolute value
l2
norm: Euclidian distance
lp
norm: (p >= 1)
l0
norm: number of non-zero entries
19
20. Least Absolute Shrinkage & Selection Operator
A linear model that estimates sparse coefficients
Uses l1
norm regularization.
20
21. Elastic Net
A linear regression model trained with l1
and l2
norms
regularization.
Elastic-net is useful when there are multiple features which are
correlated with one another. Lasso is likely to pick one of these at
random, while elastic-net is likely to pick both.
21
23. Least Angle RegreSsion
A regression algorithm for high-dimensional data.
1. Start with all coefficients equal to zero
2. Find the predictor most correlated with the response, say xj1
.
3. Take the largest step possible in the direction of this predictor
4. When some other predictor, say xj2
, has as much correlation
with the current residual, proceed in a direction equiangular
between the two predictors.
23
24. Feature Extraction
24
Generate synthetic (read: made up) features that
represent the available ones.
Methods to cover:
1. PCA
2. t-SNE
3. Spectral Embedding
4. LDA
Curse Of Dimensionality
Why Reduce Dimensions
Types of Dimensionality
Reduction
● Feature Selection
● Feature Extraction
Application Specific
Methods
25. Principal Component Analysis
Decomposes a dataset into a set of successive orthogonal
components that explain a maximum amount of variance.
25
Demo: http://setosa.io/ev/principal-component-analysis
26. Singular Value Decomposition
Factorization of A into the product of three matrices.
The columns of U and V are orthonormal and the matrix D is
diagonal with positive real entries.
26
28. Singular Value Decomposition
28
Singular value decomposition is essentially trying to reduce a rank
d matrix to a rank r matrix.
We can take a list of d unique vectors, and approximate them as a
linear combination of r unique vectors.
29. t-Stochastic Neighbor Embedding
1. Computes probabilities that are proportional to the similarity
of objects.
2. Uses the probabilities to learn a d dimensional map that
reflects the similarities as well as possible.
3. Minimize the Kullback–Leibler divergence.
29
Demo: https://distill.pub/2016/misread-tsne/
31. Spectral Embedding
Example of nonlinear dimensionality reduction.
1. Weighted Graph Construction
Transform the raw input data into graph representation using
affinity (adjacency) matrix representation.
2. Graph Laplacian Construction
3. Partial Eigenvalue Decomposition
Eigenvalue decomposition is done on graph Laplacian
31
32. Linear Discriminant Analysis
Use classifier coefficients to calculate a projection of the data in k
dimensions.
Ensure that the value of k is less than C - 1. (C = number of classes)
32
33. Application Specific Methods
Simple yet effective methods of dimensionality
reduction in:
● Computer Vision
● Natural Language Processing
● Time Series
33
Curse Of Dimensionality
Why Reduce Dimensions
Types of Dimensionality
Reduction
● Feature Selection
● Feature Extraction
Application Specific
Methods
34. Computer Vision
Applications:
● Facial Recognition
● Self Driving Cars
● Optical Character Recognition (OCR)
Data: matrix of pixels’ (RGB or Grayscale) values
34
35. Pooling
Pooling algorithm: max, average, other statistical metrics
Filter: patch to apply the pooling algorithm to
Stride: the step distance to the next patch
35
37. Removing Words
Stopwords are common terms that do not add significance
relative to the machine learning application.
Ex. the, a, an, he, she, because, not
Stopword removal is very common in NLP applications but
selecting stopwords may be difficult.
37
38. Combining Words
Reducing words to their
stem/root word.
Examples:
● presumably -> presum
● multiply -> multipli
● crying -> cri
38
Similar to stemming but includes
parts of speech.
Examples:
● hike_verb
● hike_noun
Stemming Lemmatization
Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo.
45. Relevant Wikipedia Pages
Norms
Curse of Dimensionality
Dimensionality Reduction
Feature Selection
Feature Extraction
Feature Engineering
45
Mutual Information
LASSO
Ridge Regression
Elastic Nets
LARS
SVD
PCA
t-SNE
LDA
46. Relevant Wikipedia Pages
Computer Vision
Natural Language Processing
Time Series
46
Pooling Layers
Stopwords
Stemming
Lemmatization
Granger Causality
