Near Oracle Performance and Block Analysis of Signal Space Greedy Methods

Authors

Raja Giryes
Deanna Needell, Claremont McKenna CollegeFollow

Document Type

Article - preprint

Department

Mathematics (CMC)

Publication Date

7-28-2014

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 support 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.

Comments

Featured in Journal of Approximation Theory, vol. 194, 157 - 174, 2015.

Rights Information

© 2014 Giryes, Needell

Terms of Use & License Information

Terms of Use for work posted in Scholarship@Claremont.

Recommended Citation

Giryes, R., Needell, D., "Near Oracle Performance and Block Analysis of Signal Space Greedy Methods", Submitted, arXiv preprint arXiv:1402.2601, 2014.