Your browser doesn't support javascript.
loading
Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation.
IEEE Trans Image Process ; 33: 2835-2850, 2024.
Article en En | MEDLINE | ID: mdl-38598373
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.

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: IEEE Trans Image Process Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: IEEE Trans Image Process Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article