Robust Low-Rank Tensor Minimization via a New Tensor Spectral k -Support Norm

Recently, based on a new tensor algebraic framework for third-order tensors, the tensor singular value decomposition (t-SVD) and its associated tubal rank definition have shed new light on low-rank tensor modeling. Its applications to robust image/video recovery and background modeling show promisin...

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Vydáno v:IEEE transactions on image processing Ročník 29; s. 2314 - 2327
Hlavní autoři: Lou, Jian, Cheung, Yiu-Ming
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2020
Témata:
alternating direction method of multipliers
Computational modeling
Computer science
Computer vision
conditional gradient descent
Minimization
Optimization
proximal algorithm
Robust low-rank tensor minimization
Task analysis
tensor robust principal component analysis
tensor singular value decomposition (t-SVD)
ISSN:1057-7149, 1941-0042, 1941-0042
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Shrnutí:Recently, based on a new tensor algebraic framework for third-order tensors, the tensor singular value decomposition (t-SVD) and its associated tubal rank definition have shed new light on low-rank tensor modeling. Its applications to robust image/video recovery and background modeling show promising performance due to its superior capability in modeling cross-channel/frame information. Under the t-SVD framework, we propose a new tensor norm called tensor spectral k-support norm (TSP-k) by an alternative convex relaxation. As an interpolation between the existing tensor nuclear norm (TNN) and tensor Frobenius norm (TFN), it is able to simultaneously drive minor singular values to zero to induce low-rankness, and to capture more global information for better preserving intrinsic structure. We provide the proximal operator and the polar operator for the TSP-k norm as key optimization blocks, along with two showcase optimization algorithms for mediumand large-size tensors. Experiments on synthetic, image and video datasets in medium and large sizes, all verify the superiority of the TSP-k norm and the effectiveness of both optimization methods in comparison with the existing counterparts.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2019.2946445