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Paper Abstract and Keywords
Presentation 2018-11-05 15:10
[Poster Presentation] Inference for Logitistic Regression Mixture Model with Local Variational Approximation and Study for Variational Free Energy
Fumito Nakamura, Ryosuke Konishi (Generic Solution), Yasushi Kiyoki (Keio) IBISML2018-48
Abstract (in Japanese) (See Japanese page) 
(in English) A logistic regression mixture model (LRMM) is a mixed model of the Logistic regression model, and it is widely used in the field of psychology, sociology and marketing due to its high performance. In a conventional method, the Expectation Maximization (EM) algorithm has been often used to estimate the model.
However, the EM algorithm searches the local maximum likelihood estimator, and it is known that the maximum likelihood estimator gives worse performance than the Bayesian approach.
In this paper, we propose an algorithm to estimate the LRMM by a Local Variational Approximation (LVA), which is one of the Bayesian approach. Numerical experiments show that the LVA achieves the higher performance than the EM algorithm. Furthermore, we discuss the asymptotic behavior of a variational free energy, which is one of an evaluation index for the LVA.
Keyword (in Japanese) (See Japanese page) 
(in English) Logistic Regression Mixture Model / Local Variational Approximation / Variational Free Energy / Bayesian inference / EM algorithm / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 284, IBISML2018-48, pp. 29-36, Nov. 2018.
Paper # IBISML2018-48 
Date of Issue 2018-10-29 (IBISML) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee IBISML  
Conference Date 2018-11-05 - 2018-11-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Hokkaido Citizens Activites Center (Kaderu 2.7) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information-Based Induction Science Workshop (IBIS2018) 
Paper Information
Registration To IBISML 
Conference Code 2018-11-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Inference for Logitistic Regression Mixture Model with Local Variational Approximation and Study for Variational Free Energy 
Sub Title (in English)  
Keyword(1) Logistic Regression Mixture Model  
Keyword(2) Local Variational Approximation  
Keyword(3) Variational Free Energy  
Keyword(4) Bayesian inference  
Keyword(5) EM algorithm  
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1st Author's Name Fumito Nakamura  
1st Author's Affiliation Generic Solution Corporation (Generic Solution)
2nd Author's Name Ryosuke Konishi  
2nd Author's Affiliation Generic Solution Corporation (Generic Solution)
3rd Author's Name Yasushi Kiyoki  
3rd Author's Affiliation Keio University (Keio)
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Speaker Author-1 
Date Time 2018-11-05 15:10:00 
Presentation Time 180 minutes 
Registration for IBISML 
Paper # IBISML2018-48 
Volume (vol) vol.118 
Number (no) no.284 
Page pp.29-36 
#Pages
Date of Issue 2018-10-29 (IBISML) 


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