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Tensor Formats and Tensor Decompositions

Francisco Chinesta, Elías Cueto, Victor Champaney, Chady Ghnatios, Amine Ammar, Nicolas Hascoët, David González, Icíar Alfaro, Daniele Di Lorenzo, Angelo Pasquale, Dominique Baillargeat   +10 more
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Tensor decomposition-based channel selection for motor imagery-based brain-computer interfaces. [PDF]

Cogn Neurodyn
Huang Z, Wei Q.
europepmc   +1 more source

Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals

, 2022
Mitchel J, Gordon MG, Perez RK, Biederstedt E, Bueno R, Ye CJ, Kharchenko PV.   +6 more
europepmc   +1 more source

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Tensor Decompositions and Applications

SIAM Review, 2009
Summary: This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or \(N\)-way array. Decompositions of higher-order tensors (i.e., \(N\)-way arrays with \(N \geq 3\)) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra ...
Kolda, Tamara G., Bader, Brett W.
openaire   +4 more sources

Tensor-Train Decomposition

SIAM Journal on Scientific Computing, 2011
A simple nonrecursive form of the tensor decomposition in $d$ dimensions is presented. It does not inherently suffer from the curse of dimensionality, it has asymptotically the same number of parameters as the canonical decomposition, but it is stable and its computation is based on low-rank approximation of auxiliary unfolding matrices.
I. Oseledets
semanticscholar   +2 more sources

Tensor Decomposition-Based Channel Estimation for Hybrid mmWave Massive MIMO in High-Mobility Scenarios

IEEE Transactions on Communications, 2022
Massive multiple-input multiple-output (MIMO) integrated with millimeter-wave (mmWave) can provide unprecedented performance improvement for realizing future wireless communications.
Ruoyu Zhang, Lei Cheng, Shuai Wang, Yi Lou, Wen Wu, Derrick Wing Kwan Ng   +5 more
semanticscholar   +1 more source

Multiplex Transformed Tensor Decomposition for Multidimensional Image Recovery

IEEE Transactions on Image Processing, 2023
Low-rank tensor completion aims to recover the missing entries of multi-way data, which has become popular and vital in many fields such as signal processing and computer vision. It varies with different tensor decomposition frameworks.
Lanlan Feng, Ce Zhu, Zhen Long, Jiani Liu, Yipeng Liu   +4 more
semanticscholar   +1 more source

Joint Projection Learning and Tensor Decomposition-Based Incomplete Multiview Clustering

IEEE Transactions on Neural Networks and Learning Systems, 2023
Incomplete multiview clustering (IMVC) has received increasing attention since it is often that some views of samples are incomplete in reality. Most existing methods learn similarity subgraphs from original incomplete multiview data and seek complete ...
W. Lv, Chao Zhang, Huaxiong Li, Xiuyi Jia, Chunlin Chen   +4 more
semanticscholar   +1 more source

Adaptive Genetic Algorithm-Aided Neural Network With Channel State Information Tensor Decomposition for Indoor Localization

IEEE Transactions on Evolutionary Computation, 2021
Channel state information (CSI) can provide phase and amplitude of multichannel subcarrier to better describe signal propagation characteristics. Therefore, CSI has become one of the most commonly used features in indoor Wi-Fi localization.
Mu Zhou, Yuexin Long, Weiping Zhang, Q. Pu, Yong Wang, Wei Nie, W. He   +6 more
semanticscholar   +1 more source

DecomVQANet: Decomposing visual question answering deep network via tensor decomposition and regression

Pattern Recognition, 2021
The model we developed is a novel comprehensive solution to compress and accelerate the Visual Question Answering systems. In our algorithm Convolutional Neural Network is compressed with Long Short Term Memory to accelerate processing simultaneously. We
Zongwen Bai, Ying Li, M. Woźniak, Meili Zhou, Di Li   +4 more
semanticscholar   +1 more source