A Novel Regularization Based on the Error Function for Sparse Recovery

TL;DR

This paper introduces a new regularization method based on the error function that effectively approximates the $L_0$ norm for sparse recovery, outperforming existing techniques in various scenarios.

Contribution

It proposes a novel error function-based regularization framework that approximates the $L_0$ norm and can be efficiently solved with an IRL1 algorithm, improving sparse recovery performance.

Findings

Outperforms state-of-the-art sparse recovery methods

Provides a less biased alternative to $L_1$ regularization

Demonstrates effective approximation of $L_0$ norm

Abstract

Regularization plays an important role in solving ill-posed problems by adding extra information about the desired solution, such as sparsity. Many regularization terms usually involve some vector norm, e.g., $L_1$ and $L_2$ norms. In this paper, we propose a novel regularization framework that uses the error function to approximate the unit step function. It can be considered as a surrogate function for the $L_0$ norm. The asymptotic behavior of the error function with respect to its intrinsic parameter indicates that the proposed regularization can approximate the standard $L_0$, $L_1$ norms as the parameter approaches to $0$ and $\infty,$ respectively. Statistically, it is also less biased than the $L_1$ approach. We then incorporate the error function into either a constrained or an unconstrained model when recovering a sparse signal from an under-determined linear system. Computationally, both problems can be solved via an iterative reweighted $L_1$ (IRL1) algorithm with guaranteed convergence. A large number of experimental results demonstrate that the proposed approach outperforms the state-of-the-art methods in various sparse recovery scenarios.