Paper Abstract and Keywords |
Presentation |
2020-03-02 13:00
[Poster Presentation]
A Robust Approach to Jointly-Sparse Signal Recovery Based on Minimax Concave Loss Function Kyohei Suzuki, Masahiro Yukawa (Keio Univ.) EA2019-122 SIP2019-124 SP2019-71 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
We propose a robust approach to recovering the jointly sparse signals in the presence of outliers. The proposed approach is based on (a group version of) the minimax concave (MC) loss function with two penalty terms, each of which is based on the MC function and the squared Frobenius norm, respectively. The MC-based loss and penalty functions enhance robustness and group sparsity, respectively, while the (properly-scaled) squared Frobenius norm induces the convexity. The problem is solved by the primal-dual splitting method, for which the convergence condition for the current specific case is derived with a Lipschitz constant of the gradient used in the method. Numerical examples show that the proposed approach enjoys remarkable outlier robustness. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
robustness / minimax concave function / jointly sparse signals / multiple measurement vector problem / feature selection / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 440, SIP2019-124, pp. 123-128, March 2020. |
Paper # |
SIP2019-124 |
Date of Issue |
2020-02-24 (EA, SIP, SP) |
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) |
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EA2019-122 SIP2019-124 SP2019-71 |
Conference Information |
Committee |
SP EA SIP |
Conference Date |
2020-03-02 - 2020-03-03 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Okinawa Industry Support Center |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
SIP |
Conference Code |
2020-03-SP-EA-SIP |
Language |
English |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Robust Approach to Jointly-Sparse Signal Recovery Based on Minimax Concave Loss Function |
Sub Title (in English) |
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robustness |
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minimax concave function |
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jointly sparse signals |
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multiple measurement vector problem |
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feature selection |
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1st Author's Name |
Kyohei Suzuki |
1st Author's Affiliation |
Keio University (Keio Univ.) |
2nd Author's Name |
Masahiro Yukawa |
2nd Author's Affiliation |
Keio University (Keio Univ.) |
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Speaker |
Author-1 |
Date Time |
2020-03-02 13:00:00 |
Presentation Time |
90 minutes |
Registration for |
SIP |
Paper # |
EA2019-122, SIP2019-124, SP2019-71 |
Volume (vol) |
vol.119 |
Number (no) |
no.439(EA), no.440(SIP), no.441(SP) |
Page |
pp.123-128 |
#Pages |
6 |
Date of Issue |
2020-02-24 (EA, SIP, SP) |
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