Sparse Signal Recovery With Minimization of 1-Norm Minus 2-Norm

Jinming Wen; Jian Weng; Chao Tong; Chao Ren; Zhengchun Zhou

doi:10.1109/TVT.2019.2919612

Sparse Signal Recovery With Minimization of 1-Norm Minus 2-Norm

Jinming Wen
, Jian Weng^*
, Chao Tong
, Chao Ren
, Zhengchun Zhou

^*Corresponding author for this work

School of Computer Science and Engineering

Jinan University
University of Science and Technology Beijing
Southwest Jiaotong University

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

60 Scopus citations

Abstract

The key aim of compressed sensing is to stably recover a K-sparse signals x from a linear model y = Ax + v, where v is a noise vector. Minimization of ||x||₁ - ||x||₂ is a recently proposed effective recovery method. In this paper, we show that if the mutual coherence μ of A satisfies μ < 1/3K, then this method can stably recover any K-sparse signal x based on y and A. As far as we know, this is the first sufficient condition based on mutual coherence for such method.

Original language	English
Article number	8723544
Pages (from-to)	6847-6854
Number of pages	8
Journal	IEEE Transactions on Vehicular Technology
Volume	68
Issue number	7
DOIs	https://doi.org/10.1109/TVT.2019.2919612
State	Published - Jul 2019

Keywords

Sparse signal recovery
mutual coherence
sufficient condition

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10.1109/TVT.2019.2919612

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title = "Sparse Signal Recovery With Minimization of 1-Norm Minus 2-Norm",

abstract = "The key aim of compressed sensing is to stably recover a K-sparse signals x from a linear model y = Ax + v, where v is a noise vector. Minimization of ||x||1 - ||x||2 is a recently proposed effective recovery method. In this paper, we show that if the mutual coherence μ of A satisfies μ < 1/3K, then this method can stably recover any K-sparse signal x based on y and A. As far as we know, this is the first sufficient condition based on mutual coherence for such method.",

keywords = "Sparse signal recovery, mutual coherence, sufficient condition",

author = "Jinming Wen and Jian Weng and Chao Tong and Chao Ren and Zhengchun Zhou",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.",

year = "2019",

month = jul,

doi = "10.1109/TVT.2019.2919612",

language = "英语",

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T1 - Sparse Signal Recovery With Minimization of 1-Norm Minus 2-Norm

AU - Wen, Jinming

AU - Weng, Jian

AU - Tong, Chao

AU - Ren, Chao

AU - Zhou, Zhengchun

PY - 2019/7

Y1 - 2019/7

N2 - The key aim of compressed sensing is to stably recover a K-sparse signals x from a linear model y = Ax + v, where v is a noise vector. Minimization of ||x||1 - ||x||2 is a recently proposed effective recovery method. In this paper, we show that if the mutual coherence μ of A satisfies μ < 1/3K, then this method can stably recover any K-sparse signal x based on y and A. As far as we know, this is the first sufficient condition based on mutual coherence for such method.

AB - The key aim of compressed sensing is to stably recover a K-sparse signals x from a linear model y = Ax + v, where v is a noise vector. Minimization of ||x||1 - ||x||2 is a recently proposed effective recovery method. In this paper, we show that if the mutual coherence μ of A satisfies μ < 1/3K, then this method can stably recover any K-sparse signal x based on y and A. As far as we know, this is the first sufficient condition based on mutual coherence for such method.

KW - Sparse signal recovery

KW - mutual coherence

KW - sufficient condition

UR - https://www.scopus.com/pages/publications/85069482267

U2 - 10.1109/TVT.2019.2919612

DO - 10.1109/TVT.2019.2919612

M3 - 文章

AN - SCOPUS:85069482267

SN - 0018-9545

VL - 68

SP - 6847

EP - 6854

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

IS - 7

M1 - 8723544

ER -

Sparse Signal Recovery With Minimization of 1-Norm Minus 2-Norm

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Keywords

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