On semi-supervised classification

Balaji Krishnapuram, David Williams, Ya Xue, Alex Hartemink, Lawrence Carin, Ḿario A.T. Figueiredo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

50 Scopus citations


A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple views of a given sample (e.g., multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm for training the classifier automatically adjusts the tradeoff between the contributions of: (a) the labelled data; (b) the unlabelled data; and (c) the co-training information. Active label query selection is performed using a mutual information based criterion that explicitly uses the unlabelled data and the co-training information. Encouraging results are presented on public benchmarks and on measured data from single and multiple sensors.
Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems
PublisherNeural information processing systems foundation
ISBN (Print)0262195348
StatePublished - Jan 1 2005
Externally publishedYes


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