On semi-supervised classification


Conference Paper

A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple views of a given sample (e.g., multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm for training the classifier automatically adjusts the tradeoff between the contributions of: (a) the labelled data; (b) the unlabelled data; and (c) the co-training information. Active label query selection is performed using a mutual information based criterion that explicitly uses the unlabelled data and the co-training information. Encouraging results are presented on public benchmarks and on measured data from single and multiple sensors.

Duke Authors

Cited Authors

  • Krishnapuram, B; Williams, D; Xue, Y; Hartemink, A; Carin, L; Figueiredo, MAT

Published Date

  • January 1, 2005

Published In

International Standard Serial Number (ISSN)

  • 1049-5258

International Standard Book Number 10 (ISBN-10)

  • 0262195348

International Standard Book Number 13 (ISBN-13)

  • 9780262195348

Citation Source

  • Scopus