Recovery conditions for generalized orthogonal matching pursuit based coherence

被引:0
作者
Liu, Hanbing [1 ]
Li, Chongjun [1 ]
Zhong, Yijun [2 ]
机构
[1] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Liaoning, Peoples R China
[2] Zhejiang Sci Tech Univ, Dept Math Sci, Hangzhou 310018, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Sparse recovery; Generalized orthogonal matching pursuit; Mutual coherence; Support recovery; SPARSE SIGNAL RECOVERY; STABLE RECOVERY; RECONSTRUCTION; PERFORMANCE;
D O I
10.1016/j.cam.2025.116648
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In sparse approximation, a key theoretical issue is the guarantee conditions for the exact recovery of s-sparse signals. The Orthogonal Matching Pursuit (OMP) and the Generalized Orthogonal Matching Pursuit (GOMP) are two important algorithms commonly used in sparse approximation. The main difference is that the OMP algorithm selects one atom in each iteration, while the GOMP algorithm selects multiple atoms. In the current theoretical analysis, the GOMP algorithm can only guarantee the selection of at least one correct atom in each iteration. However, in practical applications, the GOMP algorithm has been shown to select multiple correct atoms in each iteration but lacks theoretical guarantee conditions. In this paper, we discuss the extended coherence-based conditions for exact support recovery of the s-sparse signals using the GOMP algorithm. We propose several sufficient conditions for the GOMP algorithm to select M(1 <= M <= s) correct atoms in each iteration in noiseless and bounded-noise cases respectively. Some of the conditions involve the decay of nonzero entries in sparse signals. Numerical experiments demonstrate the effectiveness of the proposed sufficient conditions.
引用
收藏
页数:19
相关论文
共 50 条
  • [21] Improved Sufficient Condition for Performance Guarantee in Generalized Orthogonal Matching Pursuit
    Park, Daeyoung
    [J]. IEEE SIGNAL PROCESSING LETTERS, 2017, 24 (09) : 1308 - 1312
  • [22] Improved Sufficient Conditions for Support Recovery of Sparse Signals Via Orthogonal Matching Pursuit
    Cai, Xiaolun
    Zhou, Zhengchun
    Yang, Yang
    Wang, Yong
    [J]. IEEE ACCESS, 2018, 6 : 30437 - 30443
  • [23] Sparse Recovery With Orthogonal Matching Pursuit Under RIP
    Zhang, Tong
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2011, 57 (09) : 6215 - 6221
  • [24] On the Sparse Signal Recovery with Parallel Orthogonal Matching Pursuit
    Park, Shin-Woong
    Park, Jeonghong
    Jung, Bang Chul
    [J]. IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2013, E96A (12) : 2728 - 2730
  • [25] On The Exact Recovery Condition of Simultaneous Orthogonal Matching Pursuit
    Determe, Jean-Francois
    Louveaux, Jerome
    Jacques, Laurent
    Horlin, Francois
    [J]. IEEE SIGNAL PROCESSING LETTERS, 2016, 23 (01) : 164 - 168
  • [26] NEW COHERENCE AND RIP ANALYSIS FOR WEAK ORTHOGONAL MATCHING PURSUIT
    Yang, Mingrui
    de Hoog, Frank
    [J]. 2014 IEEE WORKSHOP ON STATISTICAL SIGNAL PROCESSING (SSP), 2014, : 376 - 379
  • [27] A Performance Guarantee for Orthogonal Matching Pursuit Using Mutual Coherence
    Mohammad Emadi
    Ehsan Miandji
    Jonas Unger
    [J]. Circuits, Systems, and Signal Processing, 2018, 37 : 1562 - 1574
  • [28] Coherence-based analysis of modified orthogonal matching pursuit using sensing dictionary
    Zhao, Juan
    Bai, Xia
    Bi, Shi-He
    Tao, Ran
    [J]. IET SIGNAL PROCESSING, 2015, 9 (03) : 218 - 225
  • [29] An improved orthogonal matching pursuit based on randomly enhanced adaptive subspace pursuit
    Zhao, Juan
    Bai, Xia
    [J]. 2017 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA ASC 2017), 2017, : 437 - 441
  • [30] COHERENCE-BASED RECOVERY GUARANTEES FOR GENERALIZED BASIS-PURSUIT DE-QUANTIZING
    Pope, Graeme
    Studer, Christoph
    Baes, Michel
    [J]. 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2012, : 3669 - 3672