Presentation	2010-03-12 Bayesian Joint Optimization for Matrix Factorization and Clustering Tikara HOSINO,
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Abstract(in English)	Statistical clustering is the method for dividing the given samples by assumed distributions. In high dimensional problems, such as text or image clustering, the direct method is suffered from over-fitting or the curse of the dimensionality. In many cases, we firstly reduce the dimensionality, then apply the clustering algorithm. However these method neglects the interaction among these two processes. In this report, we propose the hierarchical joint distribution of Latent Dirichlet Allocation and Polya Mixture and give the parameter estimation algorithm by Markov Chain Monte Carlo. Some benchmarks shows the effectiveness of the proposed method.
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Keyword(in English)	Clustering / Dimensionality reduction / Bayesian Method
Paper #	COMP2009-50
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Conference Information
Committee	COMP
Conference Date	2010/3/5(1days)
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Registration To	Theoretical Foundations of Computing (COMP)
Language	JPN
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Title (in English)	Bayesian Joint Optimization for Matrix Factorization and Clustering
Sub Title (in English)
Keyword(1)	Clustering
Keyword(2)	Dimensionality reduction
Keyword(3)	Bayesian Method
1st Author's Name	Tikara HOSINO
1st Author's Affiliation	Nihon Unisys, Ltd.()
Date	2010-03-12
Paper #	COMP2009-50
Volume (vol)	vol.109
Number (no)	465
Page	pp.pp.-
#Pages	4
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