Modified Hard Thresholding Pursuit with Regularization Assisted Support Identification

TL;DR

This paper introduces a regularized version of the Hard Thresholding Pursuit algorithm, called RHTP, which improves support identification and convergence speed in sparse recovery tasks through a regularized support selection process.

Contribution

The paper proposes RHTP, a generalized HTP algorithm that replaces support selection with a regularized approach, enhancing convergence speed and support identification capabilities.

Findings

RHTP converges faster than HTP in both noiseless and noisy scenarios.

Theoretical analysis confirms RHTP's improved support recovery performance.

Numerical experiments demonstrate the effectiveness of RHTP over traditional HTP.

Abstract

Hard thresholding pursuit (HTP) is a recently proposed iterative sparse recovery algorithm which is a result of combination of a support selection step from iterated hard thresholding (IHT) and an estimation step from the orthogonal matching pursuit (OMP). HTP has been seen to enjoy improved recovery guarantee along with enhanced speed of convergence. Much of the success of HTP can be attributed to its improved support selection capability due to the support selection step from IHT. In this paper, we propose a generalized HTP algorithm, called regularized HTP (RHTP), where the support selection step of HTP is replaced by a IHT-type support selection where the cost function is replaced by a regularized cost function, while the estimation step continues to use the least squares function. With decomposable regularizer, satisfying certain regularity conditions, the RHTP algorithm is shown to produce a sequence dynamically equivalent to a sequence evolving according to a HTP-like evolution, where the identification stage has a gradient premultiplied with a time-varying diagonal matrix. RHTP is also proven, both theoretically, and numerically, to enjoy faster convergence vis-a-vis HTP with both noiseless and noisy measurement vectors.