Multi-Task Classification for Incomplete Data

A non-parametric hierarchical Bayesian framework is developed for designing a sophisticated 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 direct handling of incomplete data. The model is further 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 analysis, and example results are presented for several data sets.