Learning classifiers on a partially labeled data manifold

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Qiuhua, L; Xuejun, L; Carin, L

Published Date

  • August 6, 2007

Published In

Volume / Issue

  • 2 /

International Standard Serial Number (ISSN)

  • 1520-6149

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2007.366312

Citation Source

  • Scopus