Enhanced joint sparsity via Iterative Support Detection

TL;DR

This paper introduces a novel non-convex joint sparsity model and an iterative support detection algorithm that improves multi-vector sparse signal recovery, outperforming existing methods in compressed sensing and feature learning.

Contribution

The paper extends iterative support detection to multi-vector estimation with a new non-convex model and adaptive convex relaxation, providing theoretical analysis and superior empirical results.

Findings

Enhanced recovery performance over state-of-the-art methods

Effective handling of multi-channel sparse Bernoulli signals

Theoretical convergence and recovery guarantees

Abstract

Joint sparsity has attracted considerable attention in recent years in many fields including sparse signal recovery in compressed sensing (CS), statistics, and machine learning. Traditional convex models suffer from the suboptimal performance though enjoying tractable computation. In this paper, we propose a new non-convex joint sparsity model, and develop a corresponding multi-stage adaptive convex relaxation algorithm. This method extends the idea of iterative support detection (ISD) from the single vector estimation to the multi-vector estimation by considering the joint sparsity prior. We provide some preliminary theoretical analysis including convergence analysis and a sufficient recovery condition. Numerical experiments from both compressive sensing and feature learning show the better performance of the proposed method in comparison with several state-of-the-art alternatives. Moreover, we demonstrate that the extension of ISD from the single vector to multi-vector estimation is not trivial. In particular, while ISD does not work well for reconstructing the signal channel sparse Bernoulli signal, it does achieve significantly improved performance when recovering the multi-channel sparse Bernoulli signal thanks to its ability of natural incorporation of the joint sparsity structure.