Abstract
The multiple measurement vector (MMV) problem with jointly sparse signals has been of recent interest across many fields and can be solved via ℓ2,1 minimization. In such applications, prior information is typically available and utilizing weights to incorporate the prior information has only been empirically shown to be advantageous. In this work, we prove theoretical guarantees for a weighted ℓ2,1 minimization approach to solving the MMV problem where the underlying signals admit a jointly sparse structure. Our theoretical findings are complemented with empirical results on simulated and real world video data.
Original language | English |
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Title of host publication | Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 |
Editors | Michael B. Matthews |
Pages | 645-649 |
Number of pages | 5 |
ISBN (Electronic) | 9781728143002 |
DOIs | |
State | Published - Nov 2019 |
Event | 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States Duration: Nov 3 2019 → Nov 6 2019 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Volume | 2019-November |
ISSN (Print) | 1058-6393 |
Conference
Conference | 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 11/3/19 → 11/6/19 |
Bibliographical note
Funding Information:1Spiceworks, Austin, TX, njdurgin@gmail.com. 2Goucher College, Baltimore, MD, rachel.grotheer@goucher.edu. 3Yale University, New Haven, CT, chenxi.huang@yale.edu. 4Colorado School of Mines, Golden, CO, shuangli@mines.edu. 5University of California, San Diego, CA, annama@ucsd.edu. 6University of California, Los Angeles, Los Angeles, CA, deanna@math.ucla.edu. 7University of Kentucky, Lexington, KY, jing.qin@uky.edu. The authors would like to thank ICERM at Brown University for hosting the initial WiSDM workshop at which this collaboration began, as well as for funding our follow-up collaboration. Needell was funded by NSF CAREER DMS #1348721 and NSF BIGDATA #1740325. Li was supported by the NSF grants CCF-1409258, CCF-1704204, and the DARPA Lagrange Program under ONR/SPAWAR contract N660011824020. Qin is supported by the NSF grant DMS-1941197.
Publisher Copyright:
© 2019 IEEE.
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications