A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data

We address statistical classifier design given a mixed training set con(cid:173) sisting of a small labelled feature set and a (generally larger) set of unlabelled features. This situation arises, e.g., for medical images, where although training features may be plentiful, expensive expertise is re(cid:173) quired to extract their class labels. We propose a classifier structure and learning algorithm that make effective use of unlabelled data to im(cid:173) prove performance. The learning is based on maximization of the total data likelihood, i.e. over both the labelled and unlabelled data sub(cid:173) sets. Two distinct EM learning algorithms are proposed, differing in the EM formalism applied for unlabelled data.