Presentation 2014-11-18
Asymptotic Analysis of Variational Bayesian Latent Dirichlet Allocation
Shinichi NAKAJIMA, Issei SATO, Masashi SUGIYAMA, Kazuho WATANABE, Hiroko KOBAYASHI,
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Abstract(in English) Latent Dirichlet allocation (LDA) is a popular generative model of various objects such as texts and images, where an object is expressed as a mixture of latent topics. In this paper, we theoretically investigate variational Bayesian (VB) learning in LDA. More specifically, we analytically derive the leading term of the VB free energy under an asymptotic setup, and show that there exist transition thresholds in Dirichlet hyperparameters around which the sparsity-inducing behavior drastically changes. Then we further theoretically reveal the notable phenomenon that VB tends to induce weaker sparsity than MAP in the LDA model, which is opposed to other models. We experimentally demonstrate the practical validity of our asymptotic theory on real-world Last.FM music data.
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Keyword(in English) latent Dirichlet allocation / variational Bayes / free energy / partially Bayes / sparsity
Paper # IBISML2014-64
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Committee IBISML
Conference Date 2014/11/10(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language ENG
Title (in Japanese) (See Japanese page)
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Title (in English) Asymptotic Analysis of Variational Bayesian Latent Dirichlet Allocation
Sub Title (in English)
Keyword(1) latent Dirichlet allocation
Keyword(2) variational Bayes
Keyword(3) free energy
Keyword(4) partially Bayes
Keyword(5) sparsity
1st Author's Name Shinichi NAKAJIMA
1st Author's Affiliation Berlin Big Data Center, Technische Universitat Berlin()
2nd Author's Name Issei SATO
2nd Author's Affiliation University of Tokyo
3rd Author's Name Masashi SUGIYAMA
3rd Author's Affiliation University of Tokyo
4th Author's Name Kazuho WATANABE
4th Author's Affiliation Toyohashi University of Technology
5th Author's Name Hiroko KOBAYASHI
5th Author's Affiliation Nikon Corporation
Date 2014-11-18
Paper # IBISML2014-64
Volume (vol) vol.114
Number (no) 306
Page pp.pp.-
#Pages 8
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