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. © 2007 IEEE.
|Original language||English (US)|
|Title of host publication||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|State||Published - Aug 6 2007|