Tensor \(N\)-tubal rank and its convex relaxation for low-rank tensor recovery

DOI10.1016/j.ins.2020.05.005zbMath1459.68181arXiv1812.00688OpenAlexW3022425365MaRDI QIDQ2023171

Ting-Zhu Huang, Teng-Yu Ji, Tai-Xiang Jiang, Tian-Hui Ma, Xi-Le Zhao, Yu-Bang Zheng

Publication date: 3 May 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1812.00688

zbMATH Keywords

alternating direction method of multipliers (ADMM)low-rank tensor recovery (LRTR)mode-\( k_1 k_2\) tensor unfoldingtensor \(N\)-tubal rankweighted sum of tensor nuclear norm (WSTNN)

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Learning and adaptive systems in artificial intelligence (68T05) Multilinear algebra, tensor calculus (15A69)

Related Items (10)

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Uses Software

• FSIM
• tproduct

Cites Work

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