Nonparametric Bayesian feature selection for multi-task learning


Journal Article

We present a nonparametric Bayesian model for multi-task learning, with a focus on feature selection in binary classification. The model jointly identifies groups of similar tasks and selects the subset of features relevant to the tasks within each group. The model employs a Dirchlet process with a beta- Bernoulli hierarchical base measure. The posterior inference is accomplished efficiently using a Gibbs sampler. Experimental results are presented on simulated as well as real data. © 2011 IEEE.

Full Text

Duke Authors

Cited Authors

  • Li, H; Liao, X; Carin, L

Published Date

  • August 18, 2011

Published In

Start / End Page

  • 2236 - 2239

International Standard Serial Number (ISSN)

  • 1520-6149

Digital Object Identifier (DOI)

  • 10.1109/ICASSP.2011.5946926

Citation Source

  • Scopus