Learning Topic Models by Belief Propagation

Latent Dirichlet allocation (LDA) is an important class of hierarchical Bayesian models for probabilistic topic modeling, which attracts worldwide interests and touches many important applications in text mining, computer vision and computational biology. This paper proposes a novel factor graph representation for LDA 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 approximation 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 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 belief propagation based on the factor graph representation.