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Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation.

Authors :
Qin W
Wang H
Zhang F
Ma W
Wang J
Huang T
Source :
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society [IEEE Trans Image Process] 2024; Vol. 33, pp. 2835-2850. Date of Electronic Publication: 2024 Apr 17.
Publication Year :
2024

Abstract

Within the tensor singular value decomposition (T-SVD) framework, existing robust low-rank tensor completion approaches have made great achievements in various areas of science and engineering. Nevertheless, these methods involve the T-SVD based low-rank approximation, which suffers from high computational costs when dealing with large-scale tensor data. Moreover, most of them are only applicable to third-order tensors. Against these issues, in this article, two efficient low-rank tensor approximation approaches fusing random projection techniques are first devised under the order-d ( d ≥ 3 ) T-SVD framework. Theoretical results on error bounds for the proposed randomized algorithms are provided. On this basis, we then further investigate the robust high-order tensor completion problem, in which a double nonconvex model along with its corresponding fast optimization algorithms with convergence guarantees are developed. Experimental results on large-scale synthetic and real tensor data illustrate that the proposed method outperforms other state-of-the-art approaches in terms of both computational efficiency and estimated precision.

Details

Language :
English
ISSN :
1941-0042
Volume :
33
Database :
MEDLINE
Journal :
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Publication Type :
Academic Journal
Accession number :
38598373
Full Text :
https://doi.org/10.1109/TIP.2024.3385284