Results 31 to 40 of about 1,664 (280)
Nuclear norm of higher-order tensors
Mathematics of Computation, 2017 23 ...
Shmuel Friedland, Lek-Heng Lim
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Weighted t-Schatten-p Norm Minimization for Real Color Image Denoising
IEEE Access, 2020 In this paper, to fully exploit the spatial and spectral correlation information, we present a new real color image denoising scheme using tensor Schatten-p norm (t-Schatten-p norm) minimization based on t-SVD to recover the underlying low-rank tensor ...
Min Liu, Xinggan Zhang, Lan Tang
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A parallel multi‐block alternating direction method of multipliers for tensor completion
IET Image Processing, 2021 This paper proposes an algorithm for the tensor completion problem of estimating multi‐linear data under the limitation of observation rate. Many tensor completion methods are based on nuclear norm minimization, they may fail to achieve the global ...
Hu Zhu +5 more
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Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering [PDF]
, 2023 Multi-view subspace clustering aims to integrate the complementary information contained in different views to facilitate data representation. Currently, low-rank representation (LRR) serves as a benchmark method.
Yigang Cen +9 more
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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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Symmetric Tensor Nuclear Norms [PDF]
SIAM Journal on Applied Algebra and Geometry, 2017 25 ...
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Dynamic MRI Reconstruction Combining Tensor Nuclear Norm and Casorati Matrix Nuclear Norm [PDF]
, 2022 Low-rank tensor models have been applied in accelerating dynamic magnetic resonance imaging (dMRI). Recently, a new tensor nuclear norm based on t-SVD has been proposed and applied to tensor completion.
Hu, Yue, Lu, Xin, Zhang, Yinghao
core
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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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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Spectral Norm and Nuclear Norm of a Third Order Tensor
CoRR, 2019 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 nuclear norms of those three ...
Liqun Qi 0001, Shenglong Hu
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