Classification with Incomplete Data Using Dirichlet Process Priors.

Published

Journal Article

A non-parametric hierarchical Bayesian framework is developed for designing a classifier, based on a mixture of simple (linear) classifiers. Each simple classifier is termed a local "expert", and the number of experts and their construction are manifested via a Dirichlet process formulation. The simple form of the "experts" allows analytical handling of incomplete data. The model is extended to allow simultaneous design of classifiers on multiple data sets, termed multi-task learning, with this also performed non-parametrically via the Dirichlet process. Fast inference is performed using variational Bayesian (VB) analysis, and example results are presented for several data sets. We also perform inference via Gibbs sampling, to which we compare the VB results.

Full Text

Duke Authors

Cited Authors

  • Wang, C; Liao, X; Carin, L; Dunson, DB

Published Date

  • March 2010

Published In

Volume / Issue

  • 11 /

Start / End Page

  • 3269 - 3311

PubMed ID

  • 23990757

Pubmed Central ID

  • 23990757

Electronic International Standard Serial Number (EISSN)

  • 1533-7928

International Standard Serial Number (ISSN)

  • 1532-4435

Language

  • eng