Jointly Sparse Signal Recovery with Prior Info

Natalie Durgin; Rachel Grotheer; Chenxi Huang; Shuang Li; Anna Ma; Deanna Needell; Jing Qin

doi:10.1109/IEEECONF44664.2019.9048818

Jointly Sparse Signal Recovery with Prior Info

Natalie Durgin, Rachel Grotheer, Chenxi Huang, Shuang Li, Anna Ma, Deanna Needell, Jing Qin

Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The multiple measurement vector (MMV) problem with jointly sparse signals has been of recent interest across many fields and can be solved via ℓ_2,1 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 ℓ_2,1 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
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	https://doi.org/10.1109/IEEECONF44664.2019.9048818
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
Volume	2019-November
ISSN (Print)	1058-6393

Conference

Conference	53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
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

Access to Document

10.1109/IEEECONF44664.2019.9048818

Cite this

Durgin, N., Grotheer, R., Huang, C., Li, S., Ma, A., Needell, D., & Qin, J. (2019). Jointly Sparse Signal Recovery with Prior Info. In M. B. Matthews (Ed.), Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 (pp. 645-649). [9048818] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2019-November). https://doi.org/10.1109/IEEECONF44664.2019.9048818

@inproceedings{d71cfc39d41c4841abf5cef5755893ca,

title = "Jointly Sparse Signal Recovery with Prior Info",

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.",

author = "Natalie Durgin and Rachel Grotheer and Chenxi Huang and Shuang Li and Anna Ma and Deanna Needell and Jing Qin",

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: {\textcopyright} 2019 IEEE.; null ; Conference date: 03-11-2019 Through 06-11-2019",

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Durgin, N, Grotheer, R, Huang, C, Li, S, Ma, A, Needell, D & Qin, J 2019, Jointly Sparse Signal Recovery with Prior Info. in MB Matthews (ed.), Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019., 9048818, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2019-November, pp. 645-649, 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019, Pacific Grove, United States, 11/3/19. https://doi.org/10.1109/IEEECONF44664.2019.9048818

Jointly Sparse Signal Recovery with Prior Info. / Durgin, Natalie; Grotheer, Rachel; Huang, Chenxi et al.

Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019. ed. / Michael B. Matthews. 2019. p. 645-649 9048818 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2019-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Huang, Chenxi

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N1 - 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.

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N2 - 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.

AB - 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.

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Jointly Sparse Signal Recovery with Prior Info

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Access to Document

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