On Semi-Supervised Classification

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.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Balaji Krishnapuram, David Williams, Ya Xue, Alexander J. Hartemink, Lawrence Carin, Mário A. T. Figueiredo
• Keywords: Co-training Information, Multiple Sensors, NIPS 2004, NIPS 2007, Unlabelled Data