Bayesian Exponential Family Harmoniums
A Bayesian Exponential Family Harmonium (BEFH) model is pre sented for topical modeling of text and multimedia data, and for “posterior” latent semant ic projection of such data for subsequent data mining tasks. BEFHs are a Bayesian approach to inferenc a d learning with the recently proposed EFH models and their variants, which enables smoot hed, robust estimation of the topicattribute coupling coefficients that are reminiscent of the smoothed topical word-probabilities in the latent Dirichlet Allocation (LDA) model. The Langevin a lgorithm conjoint with an MCMC scheme is applied for posterior inference with BEFH. An empi rical Bayes method is also developed to estimate the hyperparameters.