Machine Learning Department
School of Computer Science, Carnegie Mellon University
Bayesian Exponential Family Harmoniums
Fan Guo, Eric P. Xing
Keywords: Bayesian learning, latent semantics indexing, Markov
chain Monte Carlo, undirected graphical models
A Bayesian Exponential Family Harmonium (BEFH) model is presented for
topical modeling of text and multimedia data, and for "posterior" latent
semantic projection of such data for subsequent data mining tasks.
BEFHs are a Bayesian approach to inference and learning with the recently
proposed EFH models and their variants, which enables smoothed, robust
estimation of the topic-attribute coupling coefficients that are
reminiscent of the smoothed topical word-probabilities in the latent
Dirichlet Allocation (LDA) model. The Langevin algorithm conjoint with
an MCMC scheme is applied for posterior inference with BEFH. An empirical
Bayes method is also developed to estimate the hyperparameters.