Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
International Journal of Computer Vision
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Graph transduction via alternating minimization
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Towards a theoretical foundation for Laplacian-based manifold methods
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Optimality of belief propagation for random assignment problem
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
B-Matching for spectral clustering
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Brain state decoding for rapid image retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Graph-optimized locality preserving projections
Pattern Recognition
Semi-supervised sequence classification using abstraction augmented Markov models
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A graph-based semi-supervised learning for question semantic labeling
SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
Joint training for open-domain extraction on the web: exploiting overlap when supervision is limited
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Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
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Social content matching in MapReduce
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CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Linear programming in the semi-streaming model with application to the maximum matching problem
ICALP'11 Proceedings of the 38th international conference on Automata, languages and programming - Volume Part II
Pick your neighborhood: improving labels and neighborhood structure for label propagation
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Semi-supervised classification based on random subspace dimensionality reduction
Pattern Recognition
Graph optimization for dimensionality reduction with sparsity constraints
Pattern Recognition
Supervised neighborhood graph construction for semi-supervised classification
Pattern Recognition
Graph transduction as a noncooperative game
Neural Computation
Dense Neighborhoods on Affinity Graph
International Journal of Computer Vision
Learning neighborhoods for metric learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Linear programming in the semi-streaming model with application to the maximum matching problem
Information and Computation
Graph-based semi-supervised learning with multi-modality propagation for large-scale image datasets
Journal of Visual Communication and Image Representation
Semi-supervised learning using greedy max-cut
The Journal of Machine Learning Research
Structured exploration of who, what, when, and where in heterogeneous multimedia news sources
Proceedings of the 21st ACM international conference on Multimedia
A distributed algorithm for large-scale generalized matching
Proceedings of the VLDB Endowment
Low-rank coding with b-matching constraint for semi-supervised classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Semi-supervised learning with manifold fitted graphs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Dimensionality reduction with adaptive graph
Frontiers of Computer Science: Selected Publications from Chinese Universities
Perceptual relativity-based semi-supervised dimensionality reduction algorithm
Applied Soft Computing
Single network relational transductive learning
Journal of Artificial Intelligence Research
Editor's Choice Article: Sparse feature selection based on graph Laplacian for web image annotation
Image and Vision Computing
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Graph based semi-supervised learning (SSL) methods play an increasingly important role in practical machine learning systems. A crucial step in graph based SSL methods is the conversion of data into a weighted graph. However, most of the SSL literature focuses on developing label inference algorithms without extensively studying the graph building method and its effect on performance. This article provides an empirical study of leading semi-supervised methods under a wide range of graph construction algorithms. These SSL inference algorithms include the Local and Global Consistency (LGC) method, the Gaussian Random Field (GRF) method, the Graph Transduction via Alternating Minimization (GTAM) method as well as other techniques. Several approaches for graph construction, sparsification and weighting are explored including the popular k-nearest neighbors method (kNN) and the b-matching method. As opposed to the greedily constructed kNN graph, the b-matched graph ensures each node in the graph has the same number of edges and produces a balanced or regular graph. Experimental results on both artificial data and real benchmark datasets indicate that b-matching produces more robust graphs and therefore provides significantly better prediction accuracy without any significant change in computation time.