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

Producción científica: Conference contribution › revisión exhaustiva

1 Cita (Scopus)

Resumen

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.

Idioma original	English
Título de la publicación alojada	Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Editores	Michael B. Matthews
Páginas	645-649
Número de páginas	5
ISBN (versión digital)	9781728143002
DOI	https://doi.org/10.1109/IEEECONF44664.2019.9048818
Estado	Published - nov 2019
Evento	53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States Duración: nov 3 2019 → nov 6 2019

Serie de la publicación

Nombre	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volumen	2019-November
ISSN (versión impresa)	1058-6393

Conference

Conference	53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
País/Territorio	United States
Ciudad	Pacific Grove
Período	11/3/19 → 11/6/19

Nota bibliográfica

Publisher Copyright:
© 2019 IEEE.

Financiación

1Spiceworks, Austin, TX, [email protected]. 2Goucher College, Baltimore, MD, [email protected]. 3Yale University, New Haven, CT, [email protected]. 4Colorado School of Mines, Golden, CO, [email protected]. 5University of California, San Diego, CA, [email protected]. 6University of California, Los Angeles, Los Angeles, CA, [email protected]. 7University of Kentucky, Lexington, KY, [email protected]. 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.

Financiadores	Número del financiador
ICERM
ONR/SPAWAR	DMS-1941197, N660011824020
National Science Foundation (NSF)	CCF-1704204, 1941197, CCF-1409258, 1740325, 1348721
Defense Advanced Research Projects Agency

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Acceder al documento

10.1109/IEEECONF44664.2019.9048818

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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. En M. B. Matthews (Ed.), Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 (pp. 645-649). Artículo 9048818 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2019-November). https://doi.org/10.1109/IEEECONF44664.2019.9048818

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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 = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 ; Conference date: 03-11-2019 Through 06-11-2019",

year = "2019",

month = nov,

doi = "10.1109/IEEECONF44664.2019.9048818",

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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. En 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).

Producción científica: Conference contribution › revisión exhaustiva

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Jointly Sparse Signal Recovery with Prior Info

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ASJC Scopus subject areas

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