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Paper Abstract and Keywords
Presentation 2011-07-25 14:10
Belief Propagation for Slow Feature Analysis
Tomoki Sekiguchi (The Univ. of Tokyo), Toshiaki Omori, Masato Okada (The Univ. of Tokyo/RIKEN) NC2011-26
Abstract (in Japanese) (See Japanese page) 
(in English) The slow feature analysis (SFA) is a data analysis method that extracts
slowly varying features from time series data. In this study, we propose
an estimation algorithm of the probabilistic model of SFA that takes
into account an effect of observation noise. Recently, a probabilistic
model of the SFA has been proposed. However, the conventional algorithm
using the probabilistic SFA model approximately evaluates the likelihood
function by assuming observation noise to be zero. This means that
observed data is assumed not to include noise in the conventional
algorithm. Therefore it is not clear whether we can obtain a suitable
result when we apply this conventional algorithm to real noisy data. In
this study, we derived a likelihood function exactly in the
probabilistic SFA model, that takes into account an effect of
observation noise. From the graphical structure of the probabilistic SFA
model, we showed that it is possible to derive the likelihood function
exactly by using belief propagation. We applied our algorithm to
artificial data, and showed that it could accurately estimate the slow
feature from data including observation noise.
Keyword (in Japanese) (See Japanese page) 
(in English) Slow Feature Analysis / state-space model / belief propagation / graphical structure / probabilistic time series analysis / / /  
Reference Info. IEICE Tech. Rep., vol. 111, no. 157, NC2011-26, pp. 31-36, July 2011.
Paper # NC2011-26 
Date of Issue 2011-07-18 (NC) 
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 NC2011-26

Conference Information
Committee NC  
Conference Date 2011-07-25 - 2011-07-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Graduate School of Engineering, Kobe University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Intelligent systems and general 
Paper Information
Registration To NC 
Conference Code 2011-07-NC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Belief Propagation for Slow Feature Analysis 
Sub Title (in English)  
Keyword(1) Slow Feature Analysis  
Keyword(2) state-space model  
Keyword(3) belief propagation  
Keyword(4) graphical structure  
Keyword(5) probabilistic time series analysis  
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1st Author's Name Tomoki Sekiguchi  
1st Author's Affiliation The University of Tokyo (The Univ. of Tokyo)
2nd Author's Name Toshiaki Omori  
2nd Author's Affiliation The University of Tokyo/RIKEN (The Univ. of Tokyo/RIKEN)
3rd Author's Name Masato Okada  
3rd Author's Affiliation The University of Tokyo/RIKEN (The Univ. of Tokyo/RIKEN)
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Speaker Author-1 
Date Time 2011-07-25 14:10:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2011-26 
Volume (vol) vol.111 
Number (no) no.157 
Page pp.31-36 
#Pages
Date of Issue 2011-07-18 (NC) 


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