Fast randomized tensor singular value thresholding for low‐rank tensor optimization-Reference-Cited by-同舟云学术

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Fast randomized tensor singular value thresholding for low‐rank tensor optimization

Published:2022-04-17 Issue:6 Volume:29 Page:
ISSN:1070-5325
Container-title:Numerical Linear Algebra with Applications
language:en
Short-container-title:Numerical Linear Algebra App

Author:

Che Maolin¹^ORCID,Wang Xuezhong²,Wei Yimin³,Zhao Xile⁴

Affiliation:

1. School of Mathematics Southwestern University of Finance and Economics Chengdu People's Republic of China

2. School of Mathematics and Statistics Hexi University Zhangye People's Republic of China

3. School of Mathematical Sciences and Key Laboratory of Mathematics for Nonlinear Sciences Fudan University Shanghai People's Republic of China

4. School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu Sichuan People's Republic of China

Funder

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipality

Publisher

Wiley

Subject

Applied Mathematics,Algebra and Number Theory

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/nla.2444

Reference46 articles.

1. Robust principal component analysis?

2. Exact Matrix Completion via Convex Optimization

3. Sparse Subspace Clustering: Algorithm, Theory, and Applications

4. Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization

5. Robust Recovery of Subspace Structures by Low-Rank Representation

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1. Regularized and Structured Tensor Total Least Squares Methods with Applications;Journal of Optimization Theory and Applications;2024-08-12

2. A fast correction approach to tensor robust principal component analysis;Applied Mathematical Modelling;2024-04

3. Balanced Unfolding Induced Tensor Nuclear Norms for High-Order Tensor Completion;IEEE Transactions on Neural Networks and Learning Systems;2024

4. Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation;IEEE Transactions on Image Processing;2024

5. The Moore–Penrose inverse of tensors via the M-product;Computational and Applied Mathematics;2023-08-23

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