Results 21 to 30 of about 1,665,186 (233)
Multilayer Sparsity-Based Tensor Decomposition for Low-Rank Tensor Completion
IEEE Transactions on Neural Networks and Learning Systems, 2021 Existing methods for tensor completion (TC) have limited ability for characterizing low-rank (LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes hidden in a tensor, we propose a new multilayer sparsity-based ...
Jize Xue +5 more
semanticscholar +1 more source
Orthogonal Tensor Decompositions [PDF]
SIAM Journal on Matrix Analysis and Applications, 2001 The singular value decomposition of a real \(m\times n\) matrix can be reformulated as an orthogonal decomposition in the tensor product \(\mathbb{R}^m \otimes \mathbb{R}^n\). The present paper is concerned with possible generalizations to multiple tensor products \(\mathbb{R}^{m_1} \otimes\cdots \otimes \mathbb{R}^{m_k}\), a prime consideration being ...
openaire +2 more sources
Multi-Modal Image Fusion Based on Matrix Product State of Tensor
Frontiers in Neurorobotics, 2021 Multi-modal image fusion integrates different images of the same scene collected by different sensors into one image, making the fused image recognizable by the computer and perceived by human vision easily.
Yixiang Lu +4 more
doaj +1 more source
Design and Implementation of Tucker Decomposition Module Based on CUDA and CUBLAS [PDF]
Jisuanji gongcheng, 2019 Because tensor Tucker decomposition is widely used in image processing,face recognition,signal processing and other fields,Tucker decomposition algorithm becomes a key research object.However,the current popular Tucker decomposition algorithm needs to ...
ZHOU Qi,CHAI Xiaoli,MA Kejie,YU Zeren
doaj +1 more source
Tensor Decomposition for Signal Processing and Machine Learning [PDF]
IEEE Transactions on Signal Processing, 2016 Tensors or multiway arrays are functions of three or more indices $(i,j,k,\ldots)$—similar to matrices (two-way arrays), which are functions of two indices $(r,c)$ for (row, column).
N. Sidiropoulos +5 more
semanticscholar +1 more source
Tensor Decomposition Methods for High-dimensional Hamilton-Jacobi-Bellman Equations
SIAM Journal on Scientific Computing, 2021 . A tensor decomposition approach for the solution of high-dimensional, fully nonlinear Hamilton-Jacobi-Bellman equations arising in optimal feedback control of nonlinear dynamics is presented.
S. Dolgov, D. Kalise, K. Kunisch
semanticscholar +1 more source
Channel Estimation for Movable-Antenna MIMO Systems via Tensor Decomposition [PDF]
IEEE Wireless Communications LettersIn this letter, we investigate the channel estimation problem for MIMO wireless communication systems with movable antennas (MAs) at both the transmitter (Tx) and receiver (Rx). To achieve high channel estimation accuracy with low pilot training overhead,
Ruoyu Zhang +6 more
semanticscholar +1 more source
Deep Transfer Tensor Decomposition with Orthogonal Constraint for Recommender Systems
AAAI Conference on Artificial Intelligence, 2021 Tensor decomposition is one of the most effective techniques for multi-criteria recommendations. However, it suffers from data sparsity when dealing with three-dimensional (3D) user-item-criterion ratings.
Zhengyu Chen, Ziqing Xu, Donglin Wang
semanticscholar +1 more source
Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases [PDF]
The Web Conference, 2020 With the rapid development of knowledge bases (KBs), link prediction task, which completes KBs with missing facts, has been broadly studied in especially binary relational KBs (a.k.a knowledge graph) with powerful tensor decomposition related methods ...
Yu Liu, Quanming Yao, Yong Li
semanticscholar +1 more source
TedNet: A Pytorch Toolkit for Tensor Decomposition Networks [PDF]
Neurocomputing, 2021 Tensor Decomposition Networks (TDNs) prevail for their inherent compact architectures. To give more researchers a flexible way to exploit TDNs, we present a Pytorch toolkit named TedNet.
Y. Pan, Maolin Wang, Zenglin Xu
semanticscholar +1 more source