Distributed soft thresholding for sparse signal recovery
Abstract
In this paper, we address the problem of distributed sparse recovery of signals acquired via compressed measurements in a sensor network. We propose a new class of distributed algorithms to solve Lasso regression problems, when the communication to a fusion center is not possible, e.g., due to communication cost or privacy reasons. More precisely, we introduce a distributed iterative soft thresholding algorithm (DISTA) that consists of three steps: an averaging step, a gradient step, and a soft thresholding operation. We prove the convergence of DISTA in networks represented by regular graphs, and we compare it with existing methods in terms of performance, memory, and complexity.
Cite
@article{arxiv.1301.2130,
title = {Distributed soft thresholding for sparse signal recovery},
author = {Chiara Ravazzi and Sophie M. Fosson and Enrico Magli},
journal= {arXiv preprint arXiv:1301.2130},
year = {2013}
}
Comments
Revised version. Main improvements: extension of the convergence theorem to regular graphs; new numerical results and comparisons with other algorithms