A Bayesian approach to joint feature selection and classifier design

Balaji Krishnapuram, Alexander J. Hartemink, Lawrence Carin, Mário A.T. Figueiredo

Research output: Contribution to journalArticlepeer-review

120 Scopus citations

Abstract

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. © 2004 IEEE.
Original languageEnglish (US)
Pages (from-to)1105-1111
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume26
Issue number9
DOIs
StatePublished - Sep 1 2004
Externally publishedYes

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