Sparsification of Matrices and Compressed Sensing

TL;DR

This paper demonstrates through extensive simulations that sparsifying matrices significantly improves compressed sensing performance across various matrix types and recovery algorithms.

Contribution

It provides empirical evidence that sparsification enhances compressed sensing effectiveness, a novel insight supported by comprehensive simulation results.

Findings

Sparsification improves recovery accuracy in compressed sensing.

Sparse matrices perform comparably or better than dense matrices.

Performance gains are consistent across different algorithms.

Abstract

Compressed sensing is a signal processing technique whereby the limits imposed by the Shannon--Nyquist theorem can be exceeded provided certain conditions are imposed on the signal. Such conditions occur in many real-world scenarios, and compressed sensing has emerging applications in medical imaging, big data, and statistics. Finding practical matrix constructions and computationally efficient recovery algorithms for compressed sensing is an area of intense research interest. Many probabilistic matrix constructions have been proposed, and it is now well known that matrices with entries drawn from a suitable probability distribution are essentially optimal for compressed sensing. Potential applications have motivated the search for constructions of sparse compressed sensing matrices (i.e., matrices containing few non-zero entries). Various constructions have been proposed, and simulations suggest that their performance is comparable to that of dense matrices. In this paper, extensive simulations are presented which suggest that sparsification leads to a marked improvement in compressed sensing performance for a large class of matrix constructions and for many different recovery algorithms.