  Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model
for probabilistic topic modeling, which attracts worldwide interests and
touches on many important applications in text mining, computer vision and
computational biology. This paper represents LDA as a factor graph within the
Markov random field (MRF) framework, which enables the classic loopy belief
propagation (BP) algorithm for approximate inference and parameter estimation.
Although two commonly-used approximate inference methods, such as variational
Bayes (VB) and collapsed Gibbs sampling (GS), have gained great successes in
learning LDA, the proposed BP is competitive in both speed and accuracy as
validated by encouraging experimental results on four large-scale document data
sets. Furthermore, the BP algorithm has the potential to become a generic
learning scheme for variants of LDA-based topic models. To this end, we show
how to learn two typical variants of LDA-based topic models, such as
author-topic models (ATM) and relational topic models (RTM), using BP based on
the factor graph representation.
