1. Represents text documents as graph-of-words and extracts subgraph features through frequent subgraph mining to classify texts as a graph classification problem.
2. Uses gSpan algorithm to efficiently mine frequent subgraphs from the graph-of-words and selects the best minimum support threshold using the elbow method.
3. Evaluates on four datasets showing improved accuracy over bag-of-words models by capturing long-distance n-grams through subgraph features.
Pascual, Santiago, Antonio Bonafonte, and Joan Serrà. "SEGAN: Speech Enhancement Generative Adversarial Network." INTERSPEECH 2017.
Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.
K-Means, its Variants and its ApplicationsVarad Meru
This presentation was given by our project group at the Lead College competition at Shivaji University. Our project got the 1st Prize. We focused mainly on Rough K-Means and build a Social-Network-Recommender System based on Rough K-Means.
The Members of the Project group were -
Mansi Kulkarni,
Nikhil Ingole,
Prasad Mohite,
Varad Meru
Vishal Bhavsar.
Wonderful Experience !!!
Reference Scope Identification of Citances Using Convolutional Neural NetworkSaurav Jha
In the task of summarization of a scientific paper, a lot of information stands to be gained about a reference paper, from the papers that cite it. Automatically generating the reference scope (the span of cited text) in a reference paper, corresponding to citances (sentences in the citing papers that cite it) has great significance in preparing a structured summary of the reference paper. We treat this task as a binary classification problem, by extracting feature vectors from pairs of citances and reference sentences. These features are lexical, corpus-based, surface and knowledge-based. We extend the current feature set employed for reference-citance pair identification in the current state-of-the-art system. Using these features, we present a novel classification approach for this task, that employs a deep Convolutional Neural Network along with two boosting ensemble algorithms. We outperform the existing state-of-the- art for distinguishing between cited spans and non-cited spans of text in the reference paper.
Image Similarity Detection at Scale Using LSH and Tensorflow with Andrey GusevDatabricks
Learning over images and understanding the quality of content play an important role at Pinterest. This talk will present a Spark based system responsible for detecting near (and far) duplicate images. The system is used to improve the accuracy of recommendations and search results across a number of production surfaces at Pinterest.
At the core of the pipeline is a Spark implementation of batch LSH (locality sensitive hashing) search capable of comparing billions of items on a daily basis. This implementation replaced an older (MR/Solr/OpenCV) system, increasing throughput by 13x and decreasing runtime by 8x. A generalized Spark Batch LSH is now used outside of the image similarity context by a number of consumers. Inverted index compression using variable byte encoding, dictionary encoding, and primitives packing are some examples of what allows this implementation to scale. The second part of this talk will detail training and integration of a Tensorflow neural net with Spark, used in the candidate selection step of the system. By directly leveraging vectorization in a Spark context we can reduce the latency of the predictions and increase the throughput.
Overall, this talk will cover a scalable Spark image processing and prediction pipeline.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Pascual, Santiago, Antonio Bonafonte, and Joan Serrà. "SEGAN: Speech Enhancement Generative Adversarial Network." INTERSPEECH 2017.
Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.
K-Means, its Variants and its ApplicationsVarad Meru
This presentation was given by our project group at the Lead College competition at Shivaji University. Our project got the 1st Prize. We focused mainly on Rough K-Means and build a Social-Network-Recommender System based on Rough K-Means.
The Members of the Project group were -
Mansi Kulkarni,
Nikhil Ingole,
Prasad Mohite,
Varad Meru
Vishal Bhavsar.
Wonderful Experience !!!
Reference Scope Identification of Citances Using Convolutional Neural NetworkSaurav Jha
In the task of summarization of a scientific paper, a lot of information stands to be gained about a reference paper, from the papers that cite it. Automatically generating the reference scope (the span of cited text) in a reference paper, corresponding to citances (sentences in the citing papers that cite it) has great significance in preparing a structured summary of the reference paper. We treat this task as a binary classification problem, by extracting feature vectors from pairs of citances and reference sentences. These features are lexical, corpus-based, surface and knowledge-based. We extend the current feature set employed for reference-citance pair identification in the current state-of-the-art system. Using these features, we present a novel classification approach for this task, that employs a deep Convolutional Neural Network along with two boosting ensemble algorithms. We outperform the existing state-of-the- art for distinguishing between cited spans and non-cited spans of text in the reference paper.
Image Similarity Detection at Scale Using LSH and Tensorflow with Andrey GusevDatabricks
Learning over images and understanding the quality of content play an important role at Pinterest. This talk will present a Spark based system responsible for detecting near (and far) duplicate images. The system is used to improve the accuracy of recommendations and search results across a number of production surfaces at Pinterest.
At the core of the pipeline is a Spark implementation of batch LSH (locality sensitive hashing) search capable of comparing billions of items on a daily basis. This implementation replaced an older (MR/Solr/OpenCV) system, increasing throughput by 13x and decreasing runtime by 8x. A generalized Spark Batch LSH is now used outside of the image similarity context by a number of consumers. Inverted index compression using variable byte encoding, dictionary encoding, and primitives packing are some examples of what allows this implementation to scale. The second part of this talk will detail training and integration of a Tensorflow neural net with Spark, used in the candidate selection step of the system. By directly leveraging vectorization in a Spark context we can reduce the latency of the predictions and increase the throughput.
Overall, this talk will cover a scalable Spark image processing and prediction pipeline.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
k-Means is a rather simple but well known algorithms for grouping objects, clustering. Again all objects need to be represented as a set of numerical features. In addition the user has to specify the number of groups (referred to as k) he wishes to identify. Each object can be thought of as being represented by some feature vector in an n dimensional space, n being the number of all features used to describe the objects to cluster. The algorithm then randomly chooses k points in that vector space, these point serve as the initial centers of the clusters. Afterwards all objects are each assigned to center they are closest to. Usually the distance measure is chosen by the user and determined by the learning task. After that, for each cluster a new center is computed by averaging the feature vectors of all objects assigned to it. The process of assigning objects and recomputing centers is repeated until the process converges. The algorithm can be proven to converge after a finite number of iterations. Several tweaks concerning distance measure, initial center choice and computation of new average centers have been explored, as well as the estimation of the number of clusters k. Yet the main principle always remains the same. In this project we will discuss about K-means clustering algorithm, implementation and its application to the problem of unsupervised learning
A Mathematical Programming Approach for Selection of Variables in Cluster Ana...IJRES Journal
Data clustering is a common technique for statistical data analysis; it is defined as a class of
statistical techniques for classifying a set of observations into completely different groups. Cluster analysis
seeks to minimize group variance and maximize between group variance. In this study we formulate a
mathematical programming model that chooses the most important variables in cluster analysis. A nonlinear
binary model is suggested to select the most important variables in clustering a set of data. The idea of the
suggested model depends on clustering data by minimizing the distance between observations within groups.
Indicator variables are used to select the most important variables in the cluster analysis.
Ehtsham Elahi, Senior Research Engineer, Personalization Science and Engineer...MLconf
Spark and GraphX in the Netflix Recommender System: We at Netflix strive to deliver maximum enjoyment and entertainment to our millions of members across the world. We do so by having great content and by constantly innovating on our product. A key strategy to optimize both is to follow a data-driven method. Data allows us to find optimal approaches to applications such as content buying or our renowned personalization algorithms. But, in order to learn from this data, we need to be smart about the algorithms we use, how we apply them, and how we can scale them to our volume of data (over 50 million members and 5 billion hours streamed over three months). In this talk we describe how Spark and GraphX can be leveraged to address some of our scale challenges. In particular, we share insights and lessons learned on how to run large probabilistic clustering and graph diffusion algorithms on top of GraphX, making it possible to apply them at Netflix scale.
Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neur...Alessandro Suglia
Presentation for "Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks" at the 7th Italian Information Retrieval Workshop.
See paper: http://ceur-ws.org/Vol-1653/paper_11.pdf
Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neur...Claudio Greco
Slides for the presentation of the paper "Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks" at the 7th Italian Information Retrieval Workshop.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
2. Outline
Section 1 Introduction
Section 2 Review of the related work
Section 3 Preliminary concepts
Section 4 Proposed approaches
Section 5 Experimental evaluation
Section 6 Conclusion
References
2
3. 1. What is text mining ?
2. Bag-of-words and its issues
3. Graph-of-words - A new approach
Introduction
3
4. Introduction
What is Text mining?
Search engines
Understand user’s queries. E.g. What is Google?
Find matching websites or documents (ranking).
Product recommendation
Understand product description.
Understand product reviews. 4
5. Introduction
Bag-of-words and its issues
Definition
A text (such as a sentence or a document) is represented as the bag (multiset)
of its words.
5
6. Introduction
Bag-of-words and its issues
Example
“He likes watching action movies, she likes watching romantic movies”
⇒ [ “He”, “likes”, “watching”, “action”, “movies”, “she”, “likes”, “watching”,
“romantic”, “movies” ].
The sentence has 10 distinct words, by using indexes of the list, it can be
represented by a 10-entry vector: [ 1, 2, 2, 1, 2, 1, 2, 2, 1, 2 ]
6
7. Introduction
Bag-of-words and its issues
Problems
There are millions of n-gram features when dealing with thousands of news
articles, but only a few hundreds actually present in each article and tens
of class labels.
N-gram fails to capture word inversion and subset matching (e.g., “article
about news” vs. “news article”).
7
8. Introduction
Graph-of-words - A new approach
8
Consider the task of text categorization as a graph
classification problem.
Represent textual documents as graph-of-words
instead of traditional n-gram bag-of-words.
Extract more discriminative features that
correspond to long-distance n-grams through
frequent subgraph mining.
9. Introduction
Graph-of-words - A new approach
9
Summary:
1. Constructs a graph-of-words for each document
in the set
2. For each graphs from step 1 , extract its main
core (for cost-effective)
3. Find all frequent subgraphs size n in the
obtained set of graphs from step 2
4. Remove isomorphic subgraphs to reduce the
total number of features
5. Finally, extract n-gram features on the
remaining text
10. ● Subgraph feature mining on graph-of-words representations by Markov et
al. (2007)
Kudo and Matsumoto (2004), Matsumoto et al. (2005), Jiang et al. (2010) and
Arora et al. (2010) suggested using parse and dependency trees
representation for text categorization, but the support value (i.e. the total
number of features) was not discussed and can potentially lead to millions
of subgraphs on standard datasets.
Review of the related works
10
12. Definition
An undirected graph G = (V, E) , where
V is the set of vertices, which represents unique terms of the document
E is the set of edges, which represents co-occurrences between the terms
within a fixed-size sliding window
12
Preliminary Concepts
Graph-of-words model
13. Definition
Given two graphs G and H, an isomorphism of G and H is a bijection between the
vertex sets of G and H such that any two vertices u and v of G are adjacent in G if
and only if f(u) and f(v) are adjacent in H.
Example
13
Preliminary Concepts
Subgraph isomorphism
14. Definition
A subgraph H = (V’, E’) induced by the subset of vertices V’ ⊆ V and the subset of
edges E’ ⊆ E of graph G = (V, E) is called a k-core, where k is an integer, if and
only if: H is the maximal subgraph holds the property ∀ v ∈ V’, deg(v) >= k.
k-core: a maximal connected subgraph whose vertices are at least of degree k
within that subgraph.
main core: the k-core with the largest k.
Preliminary Concepts
K-core and main core
14
16. 1. Unsupervised feature mining using gSpan
2. Find frequent subgraphs using gSpan
3. Unsupervised support selection
4. Considered classifiers
5. Multiclass scenario
6. Main core mining using gSpan
Proposed approaches
16
17. Idea
● Considered the task of text categorization as a graph classification problem
● Representing textual documents as graph-of-words and then extracting
subgraph features to train a graph classifier.
● Each document is a separate graph-of-words and the collection of
documents thus corresponds to a set of graphs.
Proposed approaches
Unsupervised feature mining using gSpan
17
18. Given
● D = {G0
, G1
, G2
, ..., GN
} a graph dataset
● Support(g) the number of graphs (in D) in which g is a subgraph
● minSup minimum support threshold
Problem
Find any subgraph so that support(g) >= minSup
Proposed approaches
Find frequent subgraphs using gSpan
18
19. Frequent subgraph : a subgraph of multiple graph in D
Proposed approaches
Find frequent subgraphs using gSpan
19
20. Baseline solution
● Enumerate all the subgraphs and testing for isomorphism throughout the
collection => very expensive
Propose solution
● Use gSpan (graph-based Substructure pattern mining )
Proposed approaches
Find frequent subgraphs using gSpan
20
21. gSpan Idea:
1. For each graph, build a lexicographic order of all the edges using depth-first-
search (DFS) traversal
2. Assign to each of them a unique minimum DFS code.
3. Based on all these DFS codes, a hierarchical search tree is constructed at the
collection-level.
4. By pre-order traversal of this tree, gSpan discovers all frequent subgraphs
with required support.
Proposed approaches
Find frequent subgraphs using gSpan
21
22. Note :
● Given two graphs G and G’
G is isomorphic to G’ if and only if minDFS(G) = minDFS(G’)
The lower the support will result in:
1. more features
2. longer the mining
3. longer feature vector generation
4. longer learning .
Proposed approaches
Find frequent subgraphs using gSpan
22
23. Given
D = {G0
, G1
, G2
,... ,GN
} a graph dataset
Support(g) denotes the number of graphs (in D) in which g is a subgraph
minSup denotes the minimum support threshold
Proposed approaches
Unsupervised support selection (Select best minSup)
23
24. Situation
The classifier can only improve its goodness of fit with more features
=> It is likely that the lowest support will lead to the best test accuracy
As the support decreases, the number of features increases slightly up until a
point where it increases exponentially
=> This makes both the feature vector generation and the learning expensive,
especially with multiple classes.
Proposed approaches
Unsupervised support selection (Select best minSup)
24
26. Elbow method
Example: selecting the number of clusters in k-means clustering
Choose a number of clusters so that adding
another cluster doesn't give much better
modeling of the data
Proposed approaches
Unsupervised support selection (Select best minSup)
26
27. Elbow method
In our case :
Choose a minSup so that decreasing this value by a unit will :
not give much better accuracy
but increase the number of features significantly
Proposed approaches
Unsupervised support selection (Select best minSup)
27
28. Standard baseline classifiers
K-nearest neighbors (kNN) (Larkey and Croft, 1996)
Naive Bayes (NB) (McCallum and Nigam, 1998)
Linear Support Vector Machines (SVM) (Joachims, 1998)
Proposed approaches
Considered classifiers
28
29. Problem
Single support value might lead to some classes generating a tremendous
number of features ( hundreds of thousands ) and some others only a few (a few
hundreds subgraphs)
⇒ Need an extremely low support to include discriminative features for
these minority classes
⇒ Resulting in an exponential number of features because of the majority
classes.
Proposed approaches
Multiclass scenario
29
30. Solution
Mine frequent subgraphs per class using the same relative support (in %)
Then aggregate each feature set into a global one at the cost of a supervised
process (but still avoids cross validating).
Proposed approaches
Multiclass scenario
30
31. Problem
The number of features (subgraphs) to be extracted is very large when mining
frequent subgraphs directly !
How to extract discriminative features while maintaining word dependence
and retaining as much classification information as possible ?
Solution
Reduce the graphs’ size by keeping the densest subgraphs.
Proposed approaches
Main core using gSpan
31
33. 1. Datasets
2. Results
3. Unsupervised support selection
4. Distributions of mined n-grams
Experimental evaluation
33
34. Experimental evaluation
Datasets
34
● WebKB: 4 most frequent categories among labeled web pages from
various CS departments
(2,803 for training and 1,396 for test )
● R8: 8 most frequent categories of Reuters- 21578, a set of labeled news
articles from the 1987 Reuters newswire
(5,485 for training and 2,189 for test )
● LingSpam: 2,893 emails classified as spam or legitimate messages
(10 sets for 10-fold cross validation )
● Amazon: 8,000 product reviews over four different sub-collections
(books, DVDs, electronics and kitchen appliances) classified as positive
or negative
(1,600 for training and 400 for test )
35. Experimental evaluation
Datasets
35
● Multi-class document categorization : WebKB and R8
● Spam detection (Ling-Spam)
● Opinion mining (Amazon) so as to cover all the main subtasks of text
categorization
36. Table 1: Total number of features (n-grams or subgraphs) vs. number of features present only in main
cores along with the reduction of the dimension of the feature space on all four datasets.
36
Experimental evaluation
Results
37. Table 2: Test accuracy and macro-average F1-score on four standard datasets. Bold font marks the best
performance in a column * indicates statistical significance at p < 0.05 using micro sign test with regards
to the SVM baseline of the same column. MC corresponds to unsupervised feature selection using the
main core of each graph-of-words to extract n-gram and subgraph features. gSpan mining support
values are 1.6% (WebKB), 7% (R8), 4% (LingSpam) and 0.5% (Amazon).
37
Experimental evaluation
Results
38. Figure 2: Distribution of non-zero n-gram feature values before and after unsupervised feature selection
(main core retention) on R8 dataset. 38
Experimental evaluation
Results
39. Figure 3: Number of subgraph features/accuracy in test per support (%) on WebKB (left) and R8 (right)
datasets: in black, the selected support value chosen via the elbow method and in red, the accuracy in
test for the SVM baseline.
Experimental evaluation
Unsupervised support selection
39
40. Figure 4: Distribution of n-grams (standard and long-distance ones) among all the features on WebKB
dataset.
Experimental evaluation
Distribution of mined n-grams
40
41. Figure 5: Distribution of n-grams (standard and long-distance ones) among the top 5% most
discriminative features for SVM on WebKB dataset.
Experimental evaluation
Distribution of mined n-grams
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42. Conclusion
New graph-of-words approach for text mining.
Consider the problem as a graph classification
Achieved:
Extract more discriminative features that correspond to long-distance n-grams
through frequent subgraph mining
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43. References
Text Categorization as a Graph Classification Problem (François Rousseau, Emmanouil Kiagias ,Michalis Vazirgiannis )
http://www.aclweb.org/anthology/P15-1164
gSpan: Graph-Based Substructure Pattern Mining (Xifeng Yan and Jiawei Han )
http://cs.ucsb.edu/~xyan/papers/gSpan-short.pdf
Determining the number of clusters in a data set - The Elbow Method
https://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set
Graph isomorphism
https://en.wikipedia.org/wiki/Graph_isomorphism
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