Multiple Orthogonal Least Squares for Joint Sparse Recovery

Junhan Kim, Byonghyo Shim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


Joint sparse recovery aims to reconstruct multiple sparse signals having a common support using multiple measurement vectors (MMV). In this paper, we propose a robust joint sparse recovery algorithm, termed MMV multiple orthogonal least squares (MMV-MOLS). Owing to the novel identification rule that fully exploits the correlation between the measurement vectors, MMV-MOLS greatly improves the accuracy of the recovered signals over the conventional joint sparse recovery techniques. From the simulation results, we show that MMV-MOLS outperforms conventional joint sparse recovery algorithms, in both full row rank and rank deficient scenarios. In our analysis, we show that MMV-MOLS recovers any row K-sparse matrix accurately in the full row rank scenario with m = K + 1 measurements, which is, in fact, the minimum number of measurements to recover a row K-sparse matrix. In addition, we analyze the performance guarantee of the MMV-MOLS algorithm in the rank deficient scenario using the restricted isometry property (RIP).

Original languageEnglish
Title of host publication2018 IEEE International Symposium on Information Theory, ISIT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781538647806
Publication statusPublished - 2018 Aug 15
Externally publishedYes
Event2018 IEEE International Symposium on Information Theory, ISIT 2018 - Vail, United States
Duration: 2018 Jun 172018 Jun 22

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Other2018 IEEE International Symposium on Information Theory, ISIT 2018
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modelling and Simulation
  • Applied Mathematics


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