Paper Abstract and Keywords |
Presentation |
2021-06-28 17:15
Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm Yasunori Akagi, Naoki Marumo, Hideaki Kim, Takeshi Kurashima, Hiroyuki Toda (NTT) NC2021-10 IBISML2021-10 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
The importance of aggregated count data, which is calculated from the data of multiple individuals, continues to increase. Collective Graphical Model (CGM) is a probabilistic approach to the analysis of aggregated data. One of the most important operations in CGM is maximum a posteriori (MAP) inference of unobserved variables under given observations. Because the MAP inference problem for general CGMs has been shown to be NP-hard, an approach that solves an approximate problem has been proposed. However, this approach has two major drawbacks. First, the quality of the solution deteriorates when the values in the count tables are small, because the approximation becomes inaccurate. Second, since continuous relaxation is applied, the integrality constraints of the output are violated. To resolve these problems, this paper proposes a new method for MAP inference for CGMs on path graphs. Our method is based on the Difference of Convex Algorithm (DCA), which is a general methodology to minimize a function represented as the sum of a convex function and a concave function. In our algorithm, important subroutines in DCA can be efficiently calculated by minimum convex cost flow algorithms. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Aggregated Data / Collective Graphical Model / DC Algorithm / Minimum Convex Cost Flow / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 80, IBISML2021-10, pp. 70-77, June 2021. |
Paper # |
IBISML2021-10 |
Date of Issue |
2021-06-21 (NC, IBISML) |
ISSN |
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) |
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NC2021-10 IBISML2021-10 |
Conference Information |
Committee |
NC IBISML IPSJ-BIO IPSJ-MPS |
Conference Date |
2021-06-28 - 2021-06-30 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
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Paper Information |
Registration To |
IBISML |
Conference Code |
2021-06-NC-IBISML-BIO-MPS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm |
Sub Title (in English) |
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Keyword(1) |
Aggregated Data |
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Collective Graphical Model |
Keyword(3) |
DC Algorithm |
Keyword(4) |
Minimum Convex Cost Flow |
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1st Author's Name |
Yasunori Akagi |
1st Author's Affiliation |
Nippon Telegraph and Telephone Corporation (NTT) |
2nd Author's Name |
Naoki Marumo |
2nd Author's Affiliation |
Nippon Telegraph and Telephone Corporation (NTT) |
3rd Author's Name |
Hideaki Kim |
3rd Author's Affiliation |
Nippon Telegraph and Telephone Corporation (NTT) |
4th Author's Name |
Takeshi Kurashima |
4th Author's Affiliation |
Nippon Telegraph and Telephone Corporation (NTT) |
5th Author's Name |
Hiroyuki Toda |
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Nippon Telegraph and Telephone Corporation (NTT) |
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Speaker |
Author-1 |
Date Time |
2021-06-28 17:15:00 |
Presentation Time |
25 minutes |
Registration for |
IBISML |
Paper # |
NC2021-10, IBISML2021-10 |
Volume (vol) |
vol.121 |
Number (no) |
no.79(NC), no.80(IBISML) |
Page |
pp.70-77 |
#Pages |
8 |
Date of Issue |
2021-06-21 (NC, IBISML) |
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