We propose a novel, computationally efficient generative topographic model for inferring low dimensional representations of high dimensional data sets, designed to exploit data s...
Given several related learning tasks, we propose a nonparametric Bayesian model that captures task relatedness by assuming that the task parameters (i.e., predictors) share a late...
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 loc...
Chunping Wang, Xuejun Liao, Lawrence Carin, David ...
We consider Bayesian information collection, in which a measurement policy collects information to support a future decision. This framework includes ranking and selection, continu...
This study addresses the problem of unsupervised visual learning. It examines existing popular model order selection criteria before proposes two novel criteria for improving visu...