Near Oracle Performance and Block Analysis of Signal Space Greedy Methods

TL;DR

This paper analyzes the Signal Space CoSaMP algorithm's performance in noisy conditions and extends it to block-structured signals, demonstrating near-oracle recovery and improved performance in model-based compressed sensing.

Contribution

It provides the first near-oracle performance analysis of Signal Space CoSaMP with noisy measurements and introduces a block variant for structured signals, enhancing recovery capabilities.

Findings

Near-oracle recovery performance established for noisy signals.

Block variant significantly outperforms standard methods.

Numerical experiments validate theoretical improvements.

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.