Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis-Reference-Cited by-同舟云学术

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Dual-Enhanced High-Order Self-Learning Tensor Singular Value Decomposition for Robust Principal Component Analysis

Published:2024-07 Issue:7 Volume:5 Page:3564-3578
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Xu Honghui¹^ORCID,Fang Chuangjie¹^ORCID,Wang Renfang²^ORCID,Chen Shengyong³^ORCID,Zheng Jianwei¹^ORCID

Affiliation:

1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

2. College of Big Data and Software Engineering, Zhejiang Wanli University, Ningbo, China

3. College of Computer Sciences and Engineering, Tianjin University of Technology, Tianjin, China

Funder

Pioneer and Leading Goose R&D Program of Zhejiang

National Natural Science Foundation of China

Key Program of Natural Science Foundation of Zhejiang Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9078688/10599850/10460263.pdf?arnumber=10460263

Reference45 articles.

1. O-Minus Decomposition for Multi-View Tensor Subspace Clustering

2. Residual Tensor Train: A Quantum-inspired Approach for Learning Multiple Multilinear Correlations

3. Unsupervised Domain-Adaptation-Based Tensor Feature Learning With Structure Preservation

4. Multi-dimensional visual data completion via weighted hybrid graph-Laplacian

5. Effective Tensor Completion via Element-Wise Weighted Low-Rank Tensor Train With Overlapping Ket Augmentation

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