Results 251 to 260 of about 28,961 (286)

The Twist Tensor Nuclear Norm for Video Completion

IEEE Transactions on Neural Networks and Learning Systems, 2017
In this paper, we propose a new low-rank tensor model based on the circulant algebra, namely, twist tensor nuclear norm (t-TNN). The twist tensor denotes a three-way tensor representation to laterally store 2-D data slices in order. On one hand, t-TNN convexly relaxes the tensor multirank of the twist tensor in the Fourier domain, which allows an ...
Wenrui Hu, Dacheng Tao, Wensheng Zhang, Yuan Xie, Yehui Yang   +4 more
openaire   +4 more sources

Weighted tensor nuclear norm minimization for tensor completion using tensor-SVD

Pattern Recognition Letters, 2020
Abstract In this paper, we consider the tensor completion problem, which aims to estimate missing values from limited information. Our model is based on the recently proposed tensor-SVD, which uses the relationships among the color channels in an image or video recovery problem. To improve the availability of the model, we propose the weighted tensor
Yang Mu, Ping Wang, Liangfu Lu, Xuyun Zhang, Lianyong Qi   +4 more
openaire   +3 more sources

Internet traffic tensor completion with tensor nuclear norm

Computational Optimization and Applications, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Can Li, Yannan Chen, Dong-Hui Li
openaire   +1 more source

Further results on tensor nuclear norms

Calcolo, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Dynamic MRI Reconstruction Combining Tensor Nuclear Norm and Casorati Matrix Nuclear Norm

ISMRM Annual Meeting, 2023
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. Inspired by the different properties of the tensor nuclear norm (TNN) and the Casorati matrix nuclear norm (MNN), we introduce a novel dMRI ...
Yinghao Zhang, Yue Hu, Xin Lu
openaire   +1 more source

Tensor Nuclear Norm LPV Subspace Identification

IEEE Transactions on Automatic Control, 2018
Linear parameter varying (LPV) subspace identification methods suffer from an exponential growth in number of parameters to estimate. This results in problems with ill-conditioning. In literature, attempts have been made to address the ill-conditioning by using regularization. Its effectiveness hinges on suitable a priori knowledge.
Bilal Gunes, Jan-Willem van Wingerden, Michel Verhaegen   +2 more
openaire   +1 more source

Unifying tensor factorization and tensor nuclear norm approaches for low-rank tensor completion

Neurocomputing, 2021
Abstract Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of recovering missing entries. However, it has to repeatedly calculate the time-consuming singular value decomposition (SVD). To address this drawback, we, based on the tensor-tensor product (t-product), propose a new LRTC method-the unified ...
Du S., Xiao Q., Shi Y., Cucchiara R., Ma Y.   +4 more
openaire   +1 more source

Truncated nuclear norm minimization for tensor completion

2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), 2014
In this paper, a tensor n-mode matrix unfolding truncated nuclear norm is proposed, which is extended from the matrix truncated nuclear norm, to tensor completion problem. The alternating direction method of multipliers is utilized to solve this optimization problem.
Long-Ting Huang, H. C. So, Yuan Chen, Wen-Qin Wang   +3 more
openaire   +1 more source