Results 261 to 270 of about 1,664 (280)

A Mixture of Nuclear Norm and Matrix Factorization for Tensor Completion

Journal of Scientific Computing, 2017
The core problem of tensor completion is to estimate missing data via known data; for example, from \(3\)-dimensional scans. Let \(\mathcal{X}\) be a real \(N\)-mode tensor of size \(\ell_{1}\times\ell_{2}\times\cdots \times\ell_{N}\) with entries \(x_{i_{1}i_{2}\dots i_{N}}\) where the index \(i_{k}\) runs over a set \(I_{k}\) of size \(\ell_{k ...
Shangqi Gao, Qibin Fan
openaire   +1 more source

Fast Guaranteed Tensor Recovery with Adaptive Tensor Nuclear Norm

Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence
Real-world datasets like multi-spectral images and videos are naturally represented as tensors. However, limitations in data acquisition often lead to corrupted or incomplete tensor data, making tensor recovery a critical challenge. Solving this problem requires exploiting inherent structural patterns, with the low-rank property being particularly ...
Jiangjun Peng, Hailin Wang 0001, Xiangyong Cao, Shuang Xu   +3 more
openaire   +1 more source

Nuclear norm minimization and tensor completion in exploration seismology

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
We consider the problem of multidimensional seismic data signal recovery and noise attenuation. These data are multi-dimensional signals that can be described via a low-rank fourth-order tensor in the frequency-space domain. Tensor completion strategies can be used to recover unrecorded observations and to improve the signal-to-noise ratio of seismic ...
Nadia Kreimer, Mauricio D. Sacchi
openaire   +1 more source

Tensor recovery using the tensor nuclear norm based on nonconvex and nonlinear transformations

Signal Processing
Zhihui Tu, Qingtang Jiang, Xiaohuang Huang   +2 more
exaly   +2 more sources

Balanced Unfolding Induced Tensor Nuclear Norms for High-Order Tensor Completion

IEEE Transactions on Neural Networks and Learning Systems
The recently proposed tensor tubal rank has been witnessed to obtain extraordinary success in real-world tensor data completion. However, existing works usually fix the transform orientation along the third mode and may fail to turn multidimensional low-tubal-rank structure into account.
Yuning Qiu, Guoxu Zhou, Andong Wang, Qibin Zhao, Shengli Xie   +4 more
openaire   +2 more sources

Coupled Transformed Induced Tensor Nuclear Norm for Robust Tensor Completion

2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2023
Mengjie Qin, Zheyuan Lin, Minhong Wan, Chunlong Zhang, Jason Gu, Te Li   +5 more
openaire   +1 more source

Robust low-rank tensor completion via transformed tensor nuclear norm with total variation regularization

Neurocomputing, 2021
Minru Bai, Michael K Ng, Xiongjun Zhang   +2 more
exaly  

Consensus similarity learning based on tensor nuclear norm

Machine Vision and Applications, 2022
Rong Tang 0005, Gui-Fu Lu
openaire   +1 more source

Spectral norm and nuclear norm of a third order tensor

Journal of Industrial and Management Optimization, 2021
Liqun Qi, Shenglong Hu
exaly  

A Fast Tensor Completion Method Based on Tensor QR Decomposition and Tensor Nuclear Norm Minimization

IEEE Transactions on Computational Imaging, 2021
Fengsheng Wu, Chaoqian Li
exaly