Iterative Descent Group Hard Thresholding Algorithms for Block Sparsity - IMT - Institut Mines-Télécom Access content directly
Journal Articles Signal Processing Year : 2023

Iterative Descent Group Hard Thresholding Algorithms for Block Sparsity

Abstract

In this paper we consider the problem of recovering block-sparse structures in a linear regression context. Penalized mean squared criteria are generally considered in such contexts where L2,1 mixed norm penalty terms is often used as a convex alternative to the L2,0 penalty. Here, we propose an iterative block cyclic descent algorithm approach to address the case of an L2,0 penalty. We prove its convergence and illustrate its potential benefit compared to L2,1 or L2,q (0 < q <= 1) penalization. We also propose a momentum approach for accelerated convergence and an application to sensor positioning for array processing.
Fichier principal
Vignette du fichier
Cyclic_Descent_Group_Hard_thresholding_Algorithm_for_Block_Sparsity.pdf (1008.86 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04156466 , version 1 (05-07-2023)
hal-04156466 , version 2 (18-07-2023)

Identifiers

Cite

Thierry Chonavel, Abdeldjalil Aissa El Bey, Zahran Hajji. Iterative Descent Group Hard Thresholding Algorithms for Block Sparsity. Signal Processing, 2023, 212, pp.109182. ⟨10.1016/j.sigpro.2023.109182⟩. ⟨hal-04156466v2⟩
52 View
92 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More