Bayesian Sparse Topic Model

This paper presents a new Bayesian sparse learning approach to select salient lexical features for sparse topic modeling. The Bayesian learning based on latent Dirichlet allocation (LDA) is performed by incorporating the spike-and-slab priors . According to this sparse LDA (sLDA), the spike distribu...

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Bibliographic Details
Published inJournal of signal processing systems Vol. 74; no. 3; pp. 375 - 389
Main Authors Chien, Jen-Tzung, Chang, Ying-Lan
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.03.2014
Subjects
Circuits and Systems
Computer Imaging
Electrical Engineering
Engineering
Image Processing and Computer Vision
Pattern Recognition
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Vision
Feature selection
Topic model
Bayesian sparse learning
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Summary:This paper presents a new Bayesian sparse learning approach to select salient lexical features for sparse topic modeling. The Bayesian learning based on latent Dirichlet allocation (LDA) is performed by incorporating the spike-and-slab priors . According to this sparse LDA (sLDA), the spike distribution is used to select salient words while the slab distribution is applied to establish the latent topic model based on those selected relevant words. The variational inference procedure is developed to estimate prior parameters for sLDA. In the experiments on document modeling using LDA and sLDA, we find that the proposed sLDA does not only reduce the model perplexity but also reduce the memory and computation costs. Bayesian feature selection method does effectively identify relevant topic words for building sparse topic model.
ISSN:1939-8018
1939-8115
DOI:10.1007/s11265-013-0759-x