Jointly Sparse Signal Recovery with Prior Info

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 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 languageEnglish
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
Pages645-649
Number of pages5
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Country/TerritoryUnited States
CityPacific Grove
Period11/3/1911/6/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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