Robust Low-Rank Tensor Recovery with Rectification and Alignment

Low-rank tensor recovery in the presence of sparse but arbitrary errors is an important problem with many practical applications. In this work, we propose a general framework that recovers low-rank tensors, in which the data can be deformed by some unknown transformations and corrupted by arbitrary...

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Publié dans:IEEE transactions on pattern analysis and machine intelligence. - 1979. - 43(2021), 1 vom: 22. Jan., Seite 238-255
Auteur principal: Zhang, Xiaoqin (Auteur)
Autres auteurs: Wang, Di, Zhou, Zhengyuan, Ma, Yi
Format: Article en ligne
Langue:English
Publié: 2021
Accès à la collection:IEEE transactions on pattern analysis and machine intelligence
Sujets:Journal Article