Stochastic Natural Thresholding Algorithms

Rachel Grotheer; Shuang Li; Anna Ma; Deanna Needel; Jing Qin

doi:10.1109/IEEECONF59524.2023.10477079

Stochastic Natural Thresholding Algorithms

Rachel Grotheer
, Shuang Li
, Anna Ma
, Deanna Needel
, Jing Qin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Sparse signal recovery is one of the most fundamental problems in various applications, including medical imaging and remote sensing. Many greedy algorithms based on the family of hard thresholding operators have been developed to solve the sparse signal recovery problem. More recently, Natural Thresholding (NT) has been proposed with improved computational efficiency. This paper proposes and discusses convergence guarantees for stochastic natural thresholding algorithms by extending the NT from the deterministic version with linear measurements to the stochastic version with a general objective function. We also conduct various numerical experiments on linear and nonlinear measurements to demonstrate the performance of StoNT.

Original language	English
Title of host publication	Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023
Editors	Michael B. Matthews
Pages	832-836
Number of pages	5
ISBN (Electronic)	9798350325744
DOIs	https://doi.org/10.1109/IEEECONF59524.2023.10477079
State	Published - 2023
Event	57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023 - Pacific Grove, United States Duration: Oct 29 2023 → Nov 1 2023

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Conference

Conference	57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023
Country/Territory	United States
City	Pacific Grove
Period	10/29/23 → 11/1/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Funding

DN was partially supported by NSF DMS 2011140 and NSF DMS 2108479. JQ was supported by NSF DMS 1941197.

Funders	Funder number
Division of Mathematical Sciences	1941197, 2011140, 2108479
Division of Mathematical Sciences

Keywords

gradient matching pursuit
natural thresholding
sparse signal recovery
stochastic

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/IEEECONF59524.2023.10477079

Cite this

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title = "Stochastic Natural Thresholding Algorithms",

abstract = "Sparse signal recovery is one of the most fundamental problems in various applications, including medical imaging and remote sensing. Many greedy algorithms based on the family of hard thresholding operators have been developed to solve the sparse signal recovery problem. More recently, Natural Thresholding (NT) has been proposed with improved computational efficiency. This paper proposes and discusses convergence guarantees for stochastic natural thresholding algorithms by extending the NT from the deterministic version with linear measurements to the stochastic version with a general objective function. We also conduct various numerical experiments on linear and nonlinear measurements to demonstrate the performance of StoNT.",

keywords = "gradient matching pursuit, natural thresholding, sparse signal recovery, stochastic",

author = "Rachel Grotheer and Shuang Li and Anna Ma and Deanna Needel and Jing Qin",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023 ; Conference date: 29-10-2023 Through 01-11-2023",

year = "2023",

doi = "10.1109/IEEECONF59524.2023.10477079",

language = "English",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

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editor = "Matthews, \{Michael B.\}",

booktitle = "Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023",

}

Grotheer, R, Li, S, Ma, A, Needel, D & Qin, J 2023, Stochastic Natural Thresholding Algorithms. in MB Matthews (ed.), Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023. Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 832-836, 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023, Pacific Grove, United States, 10/29/23. https://doi.org/10.1109/IEEECONF59524.2023.10477079

Stochastic Natural Thresholding Algorithms. / Grotheer, Rachel; Li, Shuang; Ma, Anna et al.
Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023. ed. / Michael B. Matthews. 2023. p. 832-836 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Stochastic Natural Thresholding Algorithms

AU - Grotheer, Rachel

AU - Li, Shuang

AU - Ma, Anna

AU - Needel, Deanna

AU - Qin, Jing

PY - 2023

Y1 - 2023

N2 - Sparse signal recovery is one of the most fundamental problems in various applications, including medical imaging and remote sensing. Many greedy algorithms based on the family of hard thresholding operators have been developed to solve the sparse signal recovery problem. More recently, Natural Thresholding (NT) has been proposed with improved computational efficiency. This paper proposes and discusses convergence guarantees for stochastic natural thresholding algorithms by extending the NT from the deterministic version with linear measurements to the stochastic version with a general objective function. We also conduct various numerical experiments on linear and nonlinear measurements to demonstrate the performance of StoNT.

AB - Sparse signal recovery is one of the most fundamental problems in various applications, including medical imaging and remote sensing. Many greedy algorithms based on the family of hard thresholding operators have been developed to solve the sparse signal recovery problem. More recently, Natural Thresholding (NT) has been proposed with improved computational efficiency. This paper proposes and discusses convergence guarantees for stochastic natural thresholding algorithms by extending the NT from the deterministic version with linear measurements to the stochastic version with a general objective function. We also conduct various numerical experiments on linear and nonlinear measurements to demonstrate the performance of StoNT.

KW - gradient matching pursuit

KW - natural thresholding

KW - sparse signal recovery

KW - stochastic

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DO - 10.1109/IEEECONF59524.2023.10477079

M3 - Conference contribution

AN - SCOPUS:85190386641

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 832

EP - 836

BT - Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023

A2 - Matthews, Michael B.

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Y2 - 29 October 2023 through 1 November 2023

ER -

Stochastic Natural Thresholding Algorithms

Abstract

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Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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