On Semi-Supervised Classification

Abstract

A graph-based prior is proposed for parametric semi-supervised classi- fication. The prior utilizes both labelled and unlabelled data; it also in- tegrates 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 be- tween the contributions of: (a) the labelled data; (b) the unlabelled data; and (c) the co-training information. Active label query selection is per- formed 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.