A Bayesian approach to joint feature selection and classifier design.

Journal Article

This paper adopts a Bayesian approach to simultaneously learn both an optimal nonlinear classifier and a subset of predictor variables (or features) that are most relevant to the classification task. The approach uses heavy-tailed priors to promote sparsity in the utilization of both basis functions and features; these priors act as regularizers for the likelihood function that rewards good classification on the training data. We derive an expectation-maximization (EM) algorithm to efficiently compute a maximum a posteriori (MAP) point estimate of the various parameters. The algorithm is an extension of recent state-of-the-art sparse Bayesian classifiers, which in turn can be seen as Bayesian counterparts of support vector machines. Experimental comparisons using kernel classifiers demonstrate both parsimonious feature selection and excellent classification accuracy on a range of synthetic and benchmark data sets.

Full Text

Duke Authors

Cited Authors

  • Krishnapuram, B; Hartemink, AJ; Carin, L; Figueiredo, MAT

Published Date

  • September 2004

Published In

Volume / Issue

  • 26 / 9

Start / End Page

  • 1105 - 1111

PubMed ID

  • 15742887

International Standard Serial Number (ISSN)

  • 0162-8828

Digital Object Identifier (DOI)

  • 10.1109/TPAMI.2004.55

Language

  • eng

Conference Location

  • United States