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Paper Abstract and Keywords
Presentation 2017-11-10 13:00
[Poster Presentation] Structure Learning of Graph Product Multilayer Network-shaped Gaussian Markov Random Fields
Yuya Takashina, Masato Inoue (Waseda Univ.) IBISML2017-88
Abstract (in Japanese) (See Japanese page) 
(in English) Learning the structure of graphical models is important in many fields, e.g., multivariate analysis and anomaly detection. In continuous case, the graphical lasso is a basic model to estimate the structure of Markov Random Fields (MRFs). The graphical lasso assumes that the observations obey a multivariate Gaussian distribution, and utilizes the fact that the precision matrix of a multivariate Gaussian distribution corresponds to the graph structure of a Gaussian Markov Random Field (GMRF). Besides, when a graph has a hierarchical topology, there are cases when the graph can be represented as a {em graph product} of two or more graphs. Those graphs are called Graph Product Multilayer Networks (GPMNs). We propose a structure learning approach of GMRFs, in the cases the object graph can be considered as a GPMN, through the structure estimation of each factored graph. We show the proposed method can be formalized as a maximum a posteriori (MAP) estimation of the precision matrix of the whole GMRF.
Keyword (in Japanese) (See Japanese page) 
(in English) Graphical models / Markov random fields / structure learning / graphical lasso / graph product / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 293, IBISML2017-88, pp. 383-388, Nov. 2017.
Paper # IBISML2017-88 
Date of Issue 2017-11-02 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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)
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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) Structure Learning of Graph Product Multilayer Network-shaped Gaussian Markov Random Fields 
Sub Title (in English)  
Keyword(1) Graphical models  
Keyword(2) Markov random fields  
Keyword(3) structure learning  
Keyword(4) graphical lasso  
Keyword(5) graph product  
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1st Author's Name Yuya Takashina  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Masato Inoue  
2nd Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2017-11-10 13:00:00 
Presentation Time 150 minutes 
Registration for IBISML 
Paper # IBISML2017-88 
Volume (vol) vol.117 
Number (no) no.293 
Page pp.383-388 
#Pages 6 
Date of Issue 2017-11-02 (IBISML) 

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