Non-negative Sparse Recovery via Momentum-Boosted Adaptive Thresholding Algorithm

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Original languageEnglish
Article number47
Journal / PublicationJournal of Scientific Computing
Volume101
Issue number2
Online published7 Oct 2024
Publication statusPublished - Nov 2024

Abstract

Recovering a non-negative sparse signal from an underdetermined linear system remains a challenging problem in signal processing. Despite the development of various approaches, such as non-negative least squares, as well as variants of greedy algorithms and iterative thresholding methods, their recovery performance and efficiency often fall short of practical expectations. Aiming to address this limitation, this paper first devises a momentum-boosted adaptive thresholding algorithm for non-negative sparse signal recovery. Then, we establish two sufficient conditions of stable recovery for the proposed algorithm by using the restricted isometry property and mutual coherence. Extensive tests based on synthetic and real-world data demonstrate the superiority of our approach over the state-of-the-art non-negative orthogonal greedy algorithms and iterative thresholding methods, in terms of the probability of successful recovery, phase transition, and computational attractiveness. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

Research Area(s)

  • Non-negative sparse recovery, Iterative thresholding algorithm, Restricted isometric property, Mutual coherence