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Paper Abstract and Keywords
Presentation 2017-11-09 13:00
[Poster Presentation] Real Log Canonical Threshold of Stochastic Matrix Factorization and its Application to Bayesian Learning
Naoki Hayashi, Sumio Watanabe (TokyoTech) IBISML2017-38
Abstract (in Japanese) (See Japanese page) 
(in English) In stochastic matrix factorization (SMF), we deal with problems that we predict an observed stochastic matrix as a product of two stochastic matrices. This problem is equivalent to statistically estimating the rank when a transition matrix in such a Markov chain is predicted as a lower rank stochastic matrix. From statistical point of view, since a map from parameter set to a probability distribution set is not one-to-one, it is a non-regular statistical model and theoretical prediction accuracy has not been clarified. In this paper, we mathematically evaluate the real log canonical threshold of SMF and give an upper bound
of the generalization error at Bayesian learning.
Keyword (in Japanese) (See Japanese page) 
(in English) Stochastic matrix factorization (SMF) / Singular model / Real log canonical threshold / Markov chain / Bayesian learning / Bayesian inference / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 293, IBISML2017-38, pp. 23-30, Nov. 2017.
Paper # IBISML2017-38 
Date of Issue 2017-11-02 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 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)
Download PDF IBISML2017-38

Conference Information
Committee IBISML  
Conference Date 2017-11-08 - 2017-11-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Univ. of Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information-Based Induction Science Workshop (IBIS2017) 
Paper Information
Registration To IBISML 
Conference Code 2017-11-IBISML 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Real Log Canonical Threshold of Stochastic Matrix Factorization and its Application to Bayesian Learning 
Sub Title (in English)  
Keyword(1) Stochastic matrix factorization (SMF)  
Keyword(2) Singular model  
Keyword(3) Real log canonical threshold  
Keyword(4) Markov chain  
Keyword(5) Bayesian learning  
Keyword(6) Bayesian inference  
Keyword(7)  
Keyword(8)  
1st Author's Name Naoki Hayashi  
1st Author's Affiliation Tokyo Institute of Technology (TokyoTech)
2nd Author's Name Sumio Watanabe  
2nd Author's Affiliation Tokyo Institute of Technology (TokyoTech)
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Speaker Author-1 
Date Time 2017-11-09 13:00:00 
Presentation Time 150 minutes 
Registration for IBISML 
Paper # IBISML2017-38 
Volume (vol) vol.117 
Number (no) no.293 
Page pp.23-30 
#Pages 8 
Date of Issue 2017-11-02 (IBISML) 

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