Results 21 to 30 of about 1,321 (258)
Tensor p-shrinkage nuclear norm for low-rank tensor completion [PDF]
Neurocomputing, 2020 In this paper, a new definition of tensor p-shrinkage nuclear norm (p-TNN) is proposed based on tensor singular value decomposition (t-SVD). In particular, it can be proved that p-TNN is a better approximation of the tensor average rank than the tensor nuclear norm when p < 1.
Chunsheng Liu, Hong Shan, Chunlei Chen
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Symmetric Tensor Nuclear Norms [PDF]
SIAM Journal on Applied Algebra and Geometry, 2017 25 ...
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Real Color Image Denoising Using t-Product- Based Weighted Tensor Nuclear Norm Minimization
IEEE Access, 2019 Color images can be seen as third-order tensors with column, row and color modes. Considering two inherent characteristics of a color image including the non-local self-similarity (NSS) and the cross-channel correlation, we extract non-local similar ...
Min Liu, Xinggan Zhang, Lan Tang
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Nuclear norm of higher-order tensors
Mathematics of Computation, 2017 23 ...
Friedland, Shmuel, Lim, Lek-Heng
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A Hybrid Norm for Guaranteed Tensor Recovery
Frontiers in Physics, 2022 Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating low-rankness in the spectral domain over other tensor decompositions (like Tucker decomposition), t-SVD-based tensor learning has shown promising performance and
Yihao Luo +5 more
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SURE Based Truncated Tensor Nuclear Norm Regularization for Low Rank Tensor Completion [PDF]
2020 28th European Signal Processing Conference (EUSIPCO), 2021 Low rank tensor completion aims to recover the underlying low rank tensor obtained from its partial observations, this has a wide range of applications in Signal Processing and Machine Learning. A number of recent low rank tensor methods have successfully utilised the tensor singular value decomposition method with tensor nuclear norm minimisation via ...
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A concise proof to the spectral and nuclear norm bounds through tensor partitions
Open Mathematics, 2019 On estimations of the lower and upper bounds for the spectral and nuclear norm of a tensor, Li established neat bounds for the two norms based on regular tensor partitions, and proposed a conjecture for the same bounds to be hold based on general tensor ...
Kong Xu
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IEEE Access, 2018 The robust and efficient detection of infrared small target is a key technique for infrared search and track systems. Several robust principal component analysis (RPCA)-based methods have been developed recently, which have achieved the state-of-the-art ...
Yang Sun +4 more
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Convex Recovery of Tensors Using Nuclear Norm Penalization [PDF]
, 2015 The subdifferential of convex functions of the singular spectrum of real matrices has been widely studied in matrix analysis, optimization and automatic control theory. Convex analysis and optimization over spaces of tensors is now gaining much interest due to its potential applications to signal processing, statistics and engineering. The goal of this
Chretien, Stephane, Wei, Tianwen
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Spectral norm and nuclear norm of a third order tensor
Journal of Industrial & Management Optimization, 2022 <p style="text-indent:20px;">The spectral norm and the nuclear norm of a third order tensor play an important role in the tensor completion and recovery problem. We show that the spectral norm of a third order tensor is equal to the square root of the spectral norm of three positive semi-definite biquadratic tensors, and the square roots of the ...
Qi, L, Hu, S, Xu, Y
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