Rethinking Collapsed Variational Bayes Inference for LDA
  We propose a novel interpretation of the collapsed variational Bayes inference with a zero-order Taylor expansion approximation, called CVB0 inference, for latent Dirichlet allocation (LDA). We clarify the properties of the CVB0 inference by using the alpha-divergence. We show that the CVB0 inference is composed of two different divergence projections: alpha=1 and -1. This interpretation will help shed light on CVB0 works. 
Issei Sato (The University of Tokyo), Hiroshi Nakagawa (The University   of Tokyo)
cs.LG stat.ML