Graph-Based Semi-Supervised Learning

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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