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JMLR
2010

Classification with Incomplete Data Using Dirichlet Process Priors

8 years 9 months ago
Classification with Incomplete Data Using Dirichlet Process Priors
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.
Chunping Wang, Xuejun Liao, Lawrence Carin, David
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Chunping Wang, Xuejun Liao, Lawrence Carin, David B. Dunson
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