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Low-rank tensor train for tensor robust principal component analysis

Published:2020-02 Issue: Volume:367 Page:124783
ISSN:0096-3003
Container-title:Applied Mathematics and Computation
language:en
Short-container-title:Applied Mathematics and Computation

Author:

Yang Jing-Hua,Zhao Xi-Le^ORCID,Ji Teng-Yu,Ma Tian-Hui,Huang Ting-Zhu^ORCID

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

National Postdoctoral

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Mathematics

Reference43 articles.

1. A nonconvex relaxation approach to low-rank tensor completion;Zhang;IEEE Trans. Neural Netws. Learn. Syst.,2018

2. Bayesian CP factorization of incomplete tensors with automatic rank determination;Zhao;IEEE Trans. Pattern Anal.,2015

3. Robust volume minimization-based matrix factorization for remote sensing and document clustering;Fu;IEEE Trans. Signal Process.,2016

4. Total variation and high-order total variation adaptive model for restoring blurred images with cauchy noise;Yang;Comput. Math. Appl.,2019

5. Mf-lrtc: multi-filters guided low-rank tensor coding for image restoration research article;Lu;Neurocomputing,2018

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