On unified view of nullspace-type conditions for recoveries associated with general sparsity structures

TL;DR

This paper introduces a unified framework for understanding nullspace conditions that guarantee accurate recovery of various sparse and low-rank signals using convex optimization, extending classical compressed sensing results.

Contribution

It provides a general notion of sparsity structures and derives unified nullspace conditions and error bounds for different recovery models, including block-sparsity and low-rank recovery.

Findings

Unified nullspace conditions for multiple sparsity models

Verifiable sufficient conditions for exact and approximate recovery

Error bounds for noisy and nearly sparse signals

Abstract

We discuss a general notion of "sparsity structure" and associated recoveries of a sparse signal from its linear image of reduced dimension possibly corrupted with noise. Our approach allows for unified treatment of (a) the "usual sparsity" and "usual $\ell_1$ recovery," (b) block-sparsity with possibly overlapping blocks and associated block-$\ell_1$ recovery, and (c) low-rank-oriented recovery by nuclear norm minimization. The proposed recovery routines are natural extensions of the usual $\ell_1$ minimization used in Compressed Sensing. Specifically we present nullspace-type sufficient conditions for the recovery to be precise on sparse signals in the noiseless case. Then we derive error bounds for imperfect (nearly sparse signal, presence of observation noise, etc.) recovery under these conditions. In all of these cases, we present efficiently verifiable sufficient conditions for the validity of the associated nullspace properties.