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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)
Download PDF 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) 
Topics (in English)  
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)  
Keyword(1) robustness  
Keyword(2) minimax concave function  
Keyword(3) jointly sparse signals  
Keyword(4) multiple measurement vector problem  
Keyword(5) 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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