Efficient Least Residual Greedy Algorithms for Sparse Recovery

Guy Leibovitz; Raja Giryes

doi:10.1109/TSP.2020.2988427

Efficient Least Residual Greedy Algorithms for Sparse Recovery

Guy Leibovitz, Raja Giryes

School of Electrical Engineering

Tel Aviv University

Research output: Contribution to journal › Article › peer-review

Abstract

We present a novel stagewise strategy for improving greedy algorithms for sparse recovery. We demonstrate its efficiency both for synthesis and analysis sparse priors, where in both cases we demonstrate its computational efficiency and competitive reconstruction accuracy. In the synthesis case, we also provide theoretical guarantees for the signal recovery that are on par with the existing perfect reconstruction bounds for the relaxation based solvers and other sophisticated greedy algorithms.

Original language	English
Article number	9072472
Pages (from-to)	3707-3722
Number of pages	16
Journal	IEEE Transactions on Signal Processing
Volume	68
DOIs	https://doi.org/10.1109/TSP.2020.2988427
State	Published - 2020

Keywords

Approximation algorithms
approximation error
compressed sensing
greedy algorithms
least mean square methods
matching pursuit algorithms

All Science Journal Classification (ASJC) codes

Signal Processing
Electrical and Electronic Engineering

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10.1109/TSP.2020.2988427

Cite this

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title = "Efficient Least Residual Greedy Algorithms for Sparse Recovery",

abstract = "We present a novel stagewise strategy for improving greedy algorithms for sparse recovery. We demonstrate its efficiency both for synthesis and analysis sparse priors, where in both cases we demonstrate its computational efficiency and competitive reconstruction accuracy. In the synthesis case, we also provide theoretical guarantees for the signal recovery that are on par with the existing perfect reconstruction bounds for the relaxation based solvers and other sophisticated greedy algorithms.",

keywords = "Approximation algorithms, approximation error, compressed sensing, greedy algorithms, least mean square methods, matching pursuit algorithms",

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doi = "10.1109/TSP.2020.2988427",

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AB - We present a novel stagewise strategy for improving greedy algorithms for sparse recovery. We demonstrate its efficiency both for synthesis and analysis sparse priors, where in both cases we demonstrate its computational efficiency and competitive reconstruction accuracy. In the synthesis case, we also provide theoretical guarantees for the signal recovery that are on par with the existing perfect reconstruction bounds for the relaxation based solvers and other sophisticated greedy algorithms.

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KW - approximation error

KW - compressed sensing

KW - greedy algorithms

KW - least mean square methods

KW - matching pursuit algorithms

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JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

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Efficient Least Residual Greedy Algorithms for Sparse Recovery

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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