Orthogonal Tensor Recovery Based on Non-Convex Regularization and Rank Estimation

Orthogonal Tensor Recovery Based on Non-Convex Regularization and Rank Estimation

In this paper, a method for orthogonal tensor recovery based on non-convex regularization and rank estimation (OTRN-RE) is proposed, which aims to accurately recover the low-rank and sparse components of the tensor. Specifically, a new low-rank tensor decomposition algorithm is designed, which can e...

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Bibliographic Details
Main Authors: Xixiang Chen, Jingjing Zheng, Li Zhao, Wei Jiang, Xiaoqin Zhang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Orthogonal tensor recovery
non-convex regularization
tensor decomposition
low-rank recovery
Online Access:https://ieeexplore.ieee.org/document/10388352/

Internet

https://ieeexplore.ieee.org/document/10388352/

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