A Topic Modeling Toolbox Using Belief Propagation

Abstract

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 introduces a topic modeling toolbox (TMBP) based on the belief propagation (BP) algorithms. TMBP toolbox is implemented by MEX C++/Matlab/Octave for either Windows 7 or Linux. Compared with existing topic modeling packages, the novelty of this toolbox lies in the BP algorithms for learning LDA-based topic models. The current version includes BP algorithms for latent Dirichlet allocation (LDA), author-topic models (ATM), relational topic models (RTM), and labeled LDA (LaLDA). This toolbox is an ongoing project and more BP-based algorithms for various topic models will be added in the near future. Interested users may also extend BP algorithms for learning more complicated topic models. The source codes are freely available under the GNU General Public Licence, Version 1.0 at https://mloss.org/software/view/399/.

4 pages

References in corpus (3)

• On Smoothing and Inference for Topic Models
• Memory-Efficient Topic Modeling
• Residual Belief Propagation for Topic Modeling

Cited by in corpus (7)

• Fast Online EM for Big Topic Modeling
• A New Approach to Speeding Up Topic Modeling
• Towards Big Topic Modeling
• Strengthening Probabilistic Graphical Models: The Purge-and-merge Algorithm
• Residual Belief Propagation for Topic Modeling
• Heron Inference for Bayesian Graphical Models
• ALBU: An approximate Loopy Belief message passing algorithm for LDA to improve performance on small data sets