Robust tensor completion using transformed tensor singular value decomposition
Copyright policy )Robust tensor completion using transformed tensor singular value decomposition
SummaryIn this article, we study robust tensor completion by using transformed tensor singular value decomposition (SVD), which employs unitary transform matrices instead of discrete Fourier transform matrix that is used in the traditional tensor SVD. The main motivation is that a lower tubal rank tensor can be obtained by using other unitary transform matrices than that by using discrete Fourier transform matrix. This would be more effective for robust tensor completion. Experimental results for hyperspectral, video and face datasets have shown that the recovery performance for the robust tensor completion problem by using transformed tensor SVD is better in peak signal‐to‐noise ratio than that by using Fourier transform and other robust tensor completion methods.
- Weifang University China (People's Republic of)
- University of Hong Kong China (People's Republic of)
- Central China Normal University China (People's Republic of)
Eigenvalues, singular values, and eigenvectors, transformed tensor singular value decomposition, sparsity, Linear transformations, semilinear transformations, Numerical methods for low-rank matrix approximation; matrix compression, Matrix completion problems, low-rank, unitary transform matrix, Multilinear algebra, tensor calculus, robust tensor completion
Eigenvalues, singular values, and eigenvectors, transformed tensor singular value decomposition, sparsity, Linear transformations, semilinear transformations, Numerical methods for low-rank matrix approximation; matrix compression, Matrix completion problems, low-rank, unitary transform matrix, Multilinear algebra, tensor calculus, robust tensor completion
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