Stochastic Natural Thresholding Algorithms

Rachel Grotheer, Shuang Li, Anna Ma, Deanna Needell, Jing Qin
6/7/2023
eess.SPcs.LGmath.NA

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Robust Sparse Signal Recovery with Outliers: A Hard Thresholding Pursuit Approach Based on LAD

80% match2026

A Unified Framework for Soft Threshold Pruning

79% match1 repo182023

Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds

78% match1 repo662018

Probabilistic Iterative Hard Thresholding for Sparse Learning

78% match1 repo2024

A Fast and Provable Algorithm for Sparse Phase Retrieval

77% match2023

A Multiscale Approach for Enhancing Weak Signal Detection

77% match2025

Cite this paper

@article{grotheer2023stochastic,
  title  = {Stochastic Natural Thresholding Algorithms},
  author = {Rachel Grotheer and Shuang Li and Anna Ma and Deanna Needell and Jing Qin},
  year   = {2023},
  eprint = {2306.04730},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.04730v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2306.04730.svg)](https://codeofpaper.com/paper/2306.04730)

Discussion