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Paper Abstract and Keywords
Presentation 2010-11-05 15:30
[Poster Presentation] Efficient Computation of Normalized Maximum Likelihood Coding for Gaussian Mixtures with Its Applications to Model Selection
So Hirai, Kenji Yamanishi (Tokyo Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) We are concerned with the issue of efficient computation of normalized maximum likelihood (NML) code-lengths for Gaussian mixture distributions with its applications to model selection.Although the NML code-length is the optimal one in that it achieves Shtarkov's minimax criterion,there is a problem that the normalization factor in the NML code-length becomes infinity in the case where data is continuous.In this paper we propose a method for computation of the NML code-length for Gaussian mixture distributions so that the normization term is bounded by a finite value and the total computation time is ${\rm O}(n^2)$($n$ is sample size).We empirically demonstrate the effectiveness of the proposed method in terms of the rate of convergence of the selected number of clusters in comparison with AIC and BIC.
Keyword (in Japanese) (See Japanese page) 
(in English) Normalized Maximum Likelihood / Minimum Description Length Principle / Gaussian Mixture Distribution / / / / /  
Reference Info. IEICE Tech. Rep., vol. 110, no. 265, IBISML2010-103, pp. 327-333, Nov. 2010.
Paper # IBISML2010-103 
Date of Issue 2010-10-28 (IBISML) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380

Conference Information
Committee IBISML  
Conference Date 2010-11-04 - 2010-11-06 
Place (in Japanese) (See Japanese page) 
Place (in English) IIS, Univ. of Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) IBIS 2010 (Workshop on Information-based Induction Sciences) 
Paper Information
Registration To IBISML 
Conference Code 2010-11-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Efficient Computation of Normalized Maximum Likelihood Coding for Gaussian Mixtures with Its Applications to Model Selection 
Sub Title (in English)  
Keyword(1) Normalized Maximum Likelihood  
Keyword(2) Minimum Description Length Principle  
Keyword(3) Gaussian Mixture Distribution  
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1st Author's Name So Hirai  
1st Author's Affiliation The University of Tokyo (Tokyo Univ.)
2nd Author's Name Kenji Yamanishi  
2nd Author's Affiliation The University of Tokyo (Tokyo Univ.)
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Speaker
Date Time 2010-11-05 15:30:00 
Presentation Time 180 
Registration for IBISML 
Paper # IEICE-IBISML2010-103 
Volume (vol) IEICE-110 
Number (no) no.265 
Page pp.327-333 
#Pages IEICE-7 
Date of Issue IEICE-IBISML-2010-10-28 


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