Classification with Incomplete Data Using Dirichlet Process Priors
Chunping Wang, Xuejun Liao, Lawrence Carin, David B. Dunson; 11(107):3269−3311, 2010.
Abstract
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.
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