Results 21 to 30 of about 28,961 (286)

Global Weighted Tensor Nuclear Norm for Tensor Robust Principal Component Analysis [PDF]

, 2022
8 ...
Libin Wang, Yulong Wang, Shiyuan Wang, Youheng Liu, Yutao Hu, Longlong Chen, Hong Chen   +6 more
openalex   +3 more sources

Weighted Tensor Nuclear Norm Minimization for Color Image Restoration [PDF]

IEEE Access, 2019
Non-local self-similarity (NLSS) is widely used as prior information in an image restoration method. In particular, a low-rankness-based prior has a significant effect on performance.
Kaito Hosono, Shunsuke Ono, Takamichi Miyata   +2 more
doaj   +2 more sources

Low-Rank Tensor Completion via Tensor Nuclear Norm With Hybrid Smooth Regularization [PDF]

IEEE Access, 2019
As a convex surrogate of tensor multi rank, recently the tensor nuclear norm (TNN) obtains promising results in the tensor completion. However, only considering the low-tubal-rank prior is not enough for recovering the target tensor, especially when the ...
Xi-Le Zhao, Xin Nie, Yu-Bang Zheng, Teng-Yu Ji, Ting-Zhu Huang   +4 more
doaj   +2 more sources

Bounds on the Spectral Norm and the Nuclear Norm of a Tensor Based on Tensor Partitions [PDF]

SIAM Journal on Matrix Analysis and Applications, 2016
Summary: It is known that computing the spectral norm and the nuclear norm of a tensor is NP-hard in general. In this paper, we provide neat bounds for the spectral norm and the nuclear norm of a tensor based on tensor partitions. The spectral norm (respectively, the nuclear norm) can be lower and upper bounded by manipulating the spectral norms ...
Zhening Li
openalex   +5 more sources

Dim and Small Target Detection Based on Local Feature Prior and Tensor Train Nuclear Norm [PDF]

IEEE Photonics Journal
When faced with complex scenes containing strong edge contours and noise, there are still more background residuals in the detection results of traditional algorithms, leading to a high false alarm rate.
Anqing Wu, Xiangsuo Fan, Lei Min, Wenlin Qin, Ling Yu   +4 more
doaj   +2 more sources

A Weighted Nuclear Norm Method for Tensor Completion

International Journal of Signal Processing, Image Processing and Pattern Recognition, 2014
In recent years, tensor completion problem has received a significant amount of attention in computer vision, data mining and neuroscience. It is the higher order generalization of matrix completion. And these can be solved by the convex relaxation which minimizes the tensor nuclear norm instead of the n-rank of the tensor.
Juan Geng, Lai‐Sheng Wang, Yitian Xu, Xiuyu Wang   +3 more
openalex   +2 more sources

Small Defects Detection of Galvanized Strip Steel via Schatten-p Norm-Based Low-Rank Tensor Decomposition [PDF]

Sensors
Accurate and efficient white-spot defects detection for the surface of galvanized strip steel is one of the most important guarantees for the quality of steel production.
Shiyang Zhou, Xuguo Yan, Huaiguang Liu, Caiyun Gong   +3 more
doaj   +2 more sources

Tensor Completion with BMD Factor Nuclear Norm Minimization [PDF]

Comment: 10 ...
繁 竹田, Mirjeta Pasha, Misha E. Kilmer, Eric L. Miller, Abani Patra   +4 more
openalex   +3 more sources

Nuclear norm regularized loop optimization for tensor network

Physical Review Research
We propose a loop optimization algorithm based on nuclear norm regularization for the tensor network. The key ingredient of this scheme is to introduce a rank penalty term proposed in the context of data processing.
Kenji Homma, Tsuyoshi Okubo, Naoki Kawashima   +2 more
doaj   +3 more sources

Norm-Attaining Tensors and Nuclear Operators [PDF]

Mediterranean Journal of Mathematics, 2022
25 pages.
Sheldon Dantas, Mingu Jung, Óscar Roldán, Abraham Rueda Zoca   +3 more
openaire   +5 more sources