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Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Learning topic models by belief propagation

Language

English

Abstract

Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interest and touches on many important applications in text mining, computer vision and computational biology. This paper represents the collapsed LDA as a factor graph, 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 success in learning LDA, the proposed BP is competitive in both speed and accuracy, as validated by encouraging experimental results on four large-scale document datasets. Furthermore, the BP algorithm has the potential to become a generic scheme for learning variants of LDA-based topic models in the collapsed space. 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 representations. © 1979-2012 IEEE.

Keywords

Bayesian networks, belief propagation, factor graph, Gibbs sampling, hierarchical Bayesian models, Latent Dirichlet allocation, Markov random fields, message passing, topic models, variational Bayes

Publication Date

2013

Source Publication Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

35

Issue

5

Start Page

1121

End Page

1134

Publisher

Institute of Electrical and Electronics Engineers

ISSN (print)

01628828

ISSN (electronic)

19393539

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