Nonparametric Bayesian feature selection for multi-task learning

Hui Li, Xuejun Liao, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages2236-2239
Number of pages4
DOIs
StatePublished - Aug 18 2011
Externally publishedYes

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