Convergence radius and sample complexity of ITKM algorithms for dictionary learning-Reference-Cited by-同舟云学术

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Convergence radius and sample complexity of ITKM algorithms for dictionary learning

Published:2018-07 Issue:1 Volume:45 Page:22-58
ISSN:1063-5203
Container-title:Applied and Computational Harmonic Analysis
language:en
Short-container-title:Applied and Computational Harmonic Analysis

Author:

Schnass Karin

Funder

FWF

BMWF

Publisher

Elsevier BV

Subject

Applied Mathematics

Reference49 articles.

1. Learning sparsely used overcomplete dictionaries via alternating minimization;Agarwal,2014

2. Exact recovery of sparsely used overcomplete dictionaries;Agarwal,2014

3. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation;Aharon;IEEE Trans. Signal Process.,2006

4. Simple, efficient, and neural algorithms for sparse coding;Arora,2015

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2. Quantitative MR Image Reconstruction Using Parameter-Specific Dictionary Learning With Adaptive Dictionary-Size and Sparsity-Level Choice;IEEE Transactions on Biomedical Engineering;2024-02

3. Dictionary learning—from local towards global and adaptive;Information and Inference: A Journal of the IMA;2023-04-04

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