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

Producción científica: Conference contribution › revisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma original	English
Título de la publicación alojada	Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023
Editores	Michael B. Matthews
Páginas	832-836
Número de páginas	5
ISBN (versión digital)	9798350325744
DOI	https://doi.org/10.1109/IEEECONF59524.2023.10477079
Estado	Published - 2023
Evento	57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023 - Pacific Grove, United States Duración: oct 29 2023 → nov 1 2023

Serie de la publicación

Nombre	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (versión impresa)	1058-6393

Conference

Conference	57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023
País/Territorio	United States
Ciudad	Pacific Grove
Período	10/29/23 → 11/1/23

Nota bibliográfica

Publisher Copyright:
© 2023 IEEE.

Financiación

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

Financiadores	Número del financiador
Division of Mathematical Sciences	1941197, 2011140, 2108479
Division of Mathematical Sciences

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Acceder al documento

10.1109/IEEECONF59524.2023.10477079

Otros archivos y enlaces

Citar esto

@inproceedings{9ff97537963f4e81b8e5d865cf3dae79,

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",

pages = "832--836",

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. En 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).

Producción científica: Conference contribution › revisión exhaustiva

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

UR - https://www.scopus.com/pages/publications/85190386641

UR - https://www.scopus.com/pages/publications/85190386641#tab=citedBy

U2 - 10.1109/IEEECONF59524.2023.10477079

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.

T2 - 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023

Y2 - 29 October 2023 through 1 November 2023

ER -

Stochastic Natural Thresholding Algorithms

Resumen

Serie de la publicación

Conference

Nota bibliográfica

Financiación

ASJC Scopus subject areas

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto