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| 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 |
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| 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 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 | IBISML2017-88 |
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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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| Keyword(7) | |
| Keyword(8) | |
| 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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