Learning Classifiers on a Partially Labeled Data Manifold

We present an algorithm for learning parametric classifiers on a partially labeled data manifold, based on a graph representation of the manifold. The unlabeled data are utilized by basing classifier learning on neighborhoods, formed via Markov random walks. The proposed algorithm yields superior performance on three benchmark data sets and the margin of improvements over existing semi-supervised algorithms is significant.