Results 21 to 30 of about 1,664 (280)

New estimations on the upper bounds for the nuclear norm of a tensor [PDF]

Journal of Inequalities and Applications, 2018
Using the orthogonal rank of the tensor, a new estimation method for the upper bounds on the nuclear norms is presented and some new tight upper bounds on the nuclear norms are established. Taking into account the structure information of the tensor, an important factor affecting the upper bounds is discussed and some corresponding properties related ...
Xu Kong, Jicheng Li, Xiaolong Wang
openaire   +4 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   +2 more sources

Tensor Completion with BMD Factor Nuclear Norm Minimization

CoRR
10 ...
Fan Tian, Mirjeta Pasha, Misha E. Kilmer, Eric L. Miller 0001, Abani Patra   +4 more
openaire   +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

Numerical stability and tensor nuclear norm

Numerische Mathematik, 2023
25 pages, 7 ...
Zhen Dai, Lek-Heng Lim
openaire   +3 more sources

Hyper-Laplacian Regularized Multi-View Subspace Clustering With a New Weighted Tensor Nuclear Norm

IEEE Access, 2021
In this paper, we present a hyper-Laplacian regularized method WHLR-MSC with a new weighted tensor nuclear norm for multi-view subspace clustering. Specifically, we firstly stack the subspace representation matrices of the different views into a tensor ...
Qingjiang Xiao, Shiqiang Du, Jinmei Song, Yao Yu, Yixuan Huang   +4 more
doaj   +1 more source

SURE based truncated tensor nuclear norm regularization for low rank tensor completion [PDF]

, 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.
Morison, Gordon, Gordon Morison
core   +1 more source

Nonlinear Transform Induced Tensor Nuclear Norm for Tensor Completion

Journal of Scientific Computing, 2022
Nonlinear transform, tensor nuclear norm, proximal alternating minimization, tensor ...
Ben-Zheng Li, Xi-Le Zhao, Teng-Yu Ji, Xiongjun Zhang, Ting-Zhu Huang   +4 more
openaire   +2 more sources

Hyperspectral-Multispectral Image Fusion via Tensor Ring and Subspace Decompositions

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Fusion from a spatially low resolution hyperspectral image (LR-HSI) and a spectrally low resolution multispectral image (MSI) to produce a high spatial-spectral HSI (HR-HSI), known as hyperspectral super resolution, has risen to a preferred topic for ...
Honghui Xu, Mengjie Qin, Sheng yong Chen, Yuhui Zheng, Jian wei Zheng   +4 more
doaj   +1 more source

On Tensor Completion via Nuclear Norm Minimization [PDF]

Foundations of Computational Mathematics, 2015
Many problems can be formulated as recovering a low-rank tensor. Although an increasingly common task, tensor recovery remains a challenging problem because of the delicacy associated with the decomposition of higher order tensors. To overcome these difficulties, existing approaches often proceed by unfolding tensors into matrices and then apply ...
Ming Yuan 0001, Cun-Hui Zhang
openaire   +3 more sources