Near oracle performance and block analysis of signal space greedy methods

Raja Giryes*, Deanna Needell

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Compressive sampling (CoSa) is a new methodology which demonstrates that sparse signals can be recovered from a small number of linear measurements. Greedy algorithms like CoSaMP have been designed for this recovery, and variants of these methods have been adapted to the case where sparsity is with respect to some arbitrary dictionary rather than an orthonormal basis. In this work we present an analysis of the so-called Signal Space CoSaMP method when the measurements are corrupted with mean-zero white Gaussian noise. We establish near-oracle performance for recovery of signals sparse in some arbitrary dictionary. In addition, we analyze the block variant of the method for signals whose supports obey a block structure, extending the method into the model-based compressed sensing framework. Numerical experiments confirm that the block method significantly outperforms the standard method in these settings.

Original languageEnglish
Pages (from-to)157-174
Number of pages18
JournalJournal of Approximation Theory
Volume194
DOIs
StatePublished - 1 Jun 2015
Externally publishedYes

Funding

FundersFunder number
Simons Foundation Collaboration274305
National Science Foundation1348721
Air Force Office of Scientific Research
Alfred P. Sloan Foundation
Azrieli Foundation

    Keywords

    • Block sparsity
    • Coherent dictionaries
    • Compressed sensing
    • Restricted isometry property
    • Signal space methods
    • Sparse approximation

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