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Paper Abstract and Keywords
Presentation 2023-03-16 13:10
[Special Talk] Group Sparse/Low-rank Modeling for Multidimensional Signal Recovery
Seisuke Kyochi (Kogakuin Univ.) IMQ2022-52 CQ2022-93 IE2022-129 MVE2022-82
Abstract (in Japanese) (See Japanese page) 
(in English) Group sparse/low-rank modeling based on the ℓ1 norm and nuclear norm has been successfully applied
to signal processing such as restoration, regression and classification of high-dimensional signals and shows its high performance. Since typical regularization functions proposed so far are, at most two-layer composite functions, precise modeling using deeper composite functions is expected to improve performance. However, realizing a practical multi-layer regularization function has been difficult because its proximity operator tends not to be closed-form. Our previous work significantly expands the potential of the GSpLr-aware modeling by epigraphical relaxation (ER). It allows us to handle a (even non-proximable) deeply-layered mixed norm minimization by decoupling it into a norm and multiple epigraphical constraints (if each proximity operator is available). This paper introduces ER and its application to high-dimensional signal recovery.
Keyword (in Japanese) (See Japanese page) 
(in English) Convex optimization / Epigraphical relaxation / Signal recovery / Structure tensor total variation / Amplitude spectrum nuclear norm / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 439, IE2022-129, pp. 156-161, March 2023.
Paper # IE2022-129 
Date of Issue 2023-03-08 (IMQ, CQ, IE, MVE) 
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)
Download PDF IMQ2022-52 CQ2022-93 IE2022-129 MVE2022-82

Conference Information
Committee IMQ IE MVE CQ  
Conference Date 2023-03-15 - 2023-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawaken Seinenkaikan (Naha-shi) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Media of five senses, Multimedia, Media experience, Picture codinge, Image media quality, Network,quality and reliability, etc（AC） 
Paper Information
Registration To IE 
Conference Code 2023-03-IMQ-IE-MVE-CQ 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Group Sparse/Low-rank Modeling for Multidimensional Signal Recovery 
Sub Title (in English)  
Keyword(1) Convex optimization  
Keyword(2) Epigraphical relaxation  
Keyword(3) Signal recovery  
Keyword(4) Structure tensor total variation  
Keyword(5) Amplitude spectrum nuclear norm  
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1st Author's Name Seisuke Kyochi  
1st Author's Affiliation Kogakuin University (Kogakuin Univ.)
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Speaker Author-1 
Date Time 2023-03-16 13:10:00 
Presentation Time 40 minutes 
Registration for IE 
Paper # IMQ2022-52, CQ2022-93, IE2022-129, MVE2022-82 
Volume (vol) vol.122 
Number (no) no.437(IMQ), no.438(CQ), no.439(IE), no.440(MVE) 
Page pp.156-161(IMQ), pp.64-69(CQ), pp.156-161(IE), pp.156-161(MVE) 
#Pages 6 
Date of Issue 2023-03-08 (IMQ, CQ, IE, MVE) 

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