Abstract

We review the literature on semi-supervised learning, which is an area in machine learning and more generally, artificial intelligence. There has been a whole spectrum of interesting ideas on how to learn from both labeled and unlabeled data, i.e. semi-supervised learning. This document is a chapter excerpt from the author’s doctoral thesis (Zhu, 2005). However the author plans to update the online version frequently to incorporate the latest development in the field. Please obtain the latest version at http://www.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf

Citations

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1824	Normalized cuts and image segmentation - Shi, Malik - 1997
1370	Latent Dirichlet allocation - Blei, Ng, et al.
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946	Combining labeled and unlabeled data with co-training - Blum, Mitchell - 1998
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519	Laplacian eigenmaps for dimensionality reduction and data representation - Belkin, Niyogi - 2003
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375	Probabilistic latent semantic analysis - Hofmann - 1999
359	Unsupervised models for named entity classification - Collins, Singer - 1999
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325	Semi-supervised learning using gaussian fields and harmonic functions - Zhu, Ghahramani, et al.
315	Exploiting generative models in discriminative classifiers - Jaakkola, Haussler - 1998
286	Learning with Local and Global Consistency - Zhou, Bousquet, et al. - 2004
261	Segmentation using eigenvectors: A unifying view - Weiss
247	Sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts - Pang, Lee - 2004
202	Learning from Labeled and Unlabeled Data Using Graph Mincuts - Blum, Chawla
202	A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research - Ando, Zhang - 2005
198	Employing EM and pool-based active learning for text classification - McCallum, Nigam - 1998
197	V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples - Belkin, Niyogi, et al. - 2006
195	Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds - Saul, Roweis - 2003
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170	A tutorial on spectral clustering - Luxburg
160	Partially labeled classification with Markov random walks - Szummer, Jaakkola - 2008
157	Analyzing the effectiveness and applicability of cotraining - Nigam, Ghani - 2000
152	Transductive learning via spectral graph partitioning - Joachims
146	Semi-supervised support vector machines - Bennet, Demiriz - 1998
138	Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data - Donoho, Grimes
134	Diffusion kernels on graphs and other discrete input spaces - Kondor, Lafferty - 2002
120	Cluster Kernels for Semi-Supervised Learning - Chapelle, Weston, et al.
118	Kernels and regularization on graphs - Smola, Kondor - 2003
117	Spectral grouping using the Nyström method - Fowlkes, Belongie, et al. - 2004
112	L: Unsupervised learning of image manifolds by semidefinite programming - Weinberger, Saul
111	Selftaught learning: transfer learning from unlabeled data - Raina, Battle, et al.
105	Colorization using optimization - Levin, Lischinski, et al. - 2004
95	Maximum entropy discrimination - Jaakkola, Meila, et al. - 1999
94	Enhancing Supervised Learning with Unlabelled Data - Goldman, Zhou - 2000
89	Semi-supervised classification by low density separation - Chapelle, Zien - 2005
89	Integrating topics and syntax - Griffiths, Steyvers, et al. - 2005
89	Learning subjective nouns using extraction pattern bootstrapping - RILOFF, WIEBE, et al. - 2003
86	Does Baum-Welch re-estimation help taggers - Elworthy - 1994
82	The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter - Castelli, Cover - 1996
82	A mixture of experts classifier with learning based on both labelled and unlabelled data - Miller, Uyar - 1997
79	Learning a kernel matrix for nonlinear dimensionality reduction - WEINBERGER, SHA, et al. - 2004