Subramanya A. Graph-Based Semi-Supervised Learning 2014 torrent

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While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied.
Introduction
Unsupervised Learning
Supervised Learning
Semi-Supervised learning (SSL)
Graph-based Semi-Supervised Learning
Inductive vs. Transductive SSL
Book Organization
Graph Construction
Problem Statement
Task-Independent Graph Construction
k-Nearest Neighbor (k-NN) and -Neighborhood Methods
Graph Construction using b-Matching
Graph Construction using Local Reconstruction
Task-Dependent Graph Construction
Inference-Driven Metric Learning (IDML)
Graph Kernels by Spectral Transform
Conclusion
Learning and Inference
Seed Supervision
Transductive Methods
Graph Cut
Gaussian Random Fields (GRF)
Local and Global Consistency (LGC)
Adsorption
Modified Adsorption (MAD)
Quadratic Criteria (QC)
Transduction with Confidence (TACO)
Information Regularization
Measure Propagation
Inductive Methods
Manifold Regularization
Results on Benchmark SSL Data Sets
Conclusions
Scalability
Large-Scale Graph Construction
Approximate Nearest Neighbor
Other Methods
Large-Scale Inference
Graph Partitioning
Inference
Scaling to Large Number of Labels
Conclusions
Applications
Text Classification
Phone Classification
Part-of-Speech Tagging
Class-Instance Acquisition
Knowledge Base Alignment
Conclusion
Future Work
Graph Construction
Learning & Inference
Scalability
Notations
Solving Modified Adsorption (MAD) Objective
Alternating Minimization
Software
Junto Label Propagation Toolkit
Bibliography
Authors' Biographies
Index
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