Semi-Supervised Learning by Gaussian Mixtures Choi, Byoung-Jeong; Chae, Youn-Seok; Choi, Woo-Young; Park, Chang-Yi; Koo, Ja-Yong;
Discriminant analysis based on Gaussian mixture models, an useful tool for multi-class classifications, can be extended to semi-supervised learning. We consider a model selection problem for a Gaussian mixture model in semi-supervised learning. More specifically, we adopt Bayesian information criterion to determine the number of subclasses in the mixture model. Through simulations, we illustrate the usefulness of the criterion.