Classification with incomplete data using dirichlet process priors

Chunping Wang, Xuejun Liao, Lawrence Carin, David B. Dunson

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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. © 2010 Shyam Visweswaran and Gregory F. Cooper.
Original languageEnglish (US)
Pages (from-to)3269-3311
Number of pages43
JournalJournal of Machine Learning Research
StatePublished - Dec 1 2010
Externally publishedYes


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