Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors

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

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Scopus citations

Abstract

While single measurement vector (SMV) models have been widely studied in signal processing, there is a surging interest in addressing the multiple measurement vectors (MMV) problem. In the MMV setting, more than one measurement vector is available and the multiple signals to be recovered share some commonalities such as a common support. Applications in which MMV is a naturally occurring phenomenon include online streaming, medical imaging, and video recovery. This work presents a stochastic iterative algorithm for the support recovery of jointly sparse corrupted MMV. We present a variant of the sparse randomized Kaczmarz algorithm for corrupted MMV and compare our proposed method with an existing Kaczmarz type algorithm for MMV problems. We also showcase the usefulness of our approach in the online (streaming) setting and provide empirical evidence that suggests the robustness of the proposed method to the number of corruptions and the distribution from which the corruptions are drawn.

Original languageEnglish
Title of host publicationAssociation for Women in Mathematics Series
Pages1-14
Number of pages14
DOIs
StatePublished - 2019

Publication series

NameAssociation for Women in Mathematics Series
Volume17
ISSN (Print)2364-5733
ISSN (Electronic)2364-5741

Bibliographical note

Publisher Copyright:
© 2019, The Author(s) and the Association for Women in Mathematics.

ASJC Scopus subject areas

  • Gender Studies
  • General Mathematics

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