Results 191 to 200 of about 1,665,186 (233)

Tensor Singular Spectrum Decomposition: Multisensor Denoising Algorithm and Application

IEEE Transactions on Instrumentation and Measurement, 2023
Realizing multisensor signal fusion and weak feature adaptive extraction is a challenging task. Therefore, a new algorithm called tensor singular spectrum decomposition (SSD) is proposed in this study for the adaptive decomposition of multisensor time ...
Jinfeng Huang, Lingli Cui
semanticscholar   +1 more source

Multiscale tensor decomposition

2016 50th Asilomar Conference on Signals, Systems and Computers, 2016
Large datasets usually contain redundant information and summarizing these datasets is important for better data interpretation. Higher-order data reduction is usually achieved through low-rank tensor approximation which assumes that the data lies near a linear subspace across each mode.
Alp Ozdemir, Mark A. Iwen, Selin Aviyente   +2 more
openaire   +1 more source

Hyperspectral Anomaly Detection Based on Tensor Ring Decomposition With Factors TV Regularization

IEEE Transactions on Geoscience and Remote Sensing, 2023
Anomaly detection in the hyperspectral image (HSI) has gradually become a hot topic in remote sensing. Recently, some tensor-based methods have been proposed to improve detection performance by exploiting the characteristic of HSI data existing in the ...
Maoyuan Feng, Wendong Chen, Yun-xiu Yang, Qin Shu, Hongxin Li, Yanqin Huang   +5 more
semanticscholar   +1 more source

Randomized Tensor Wheel Decomposition

SIAM Journal on Scientific Computing
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mengyu Wang, Yajie Yu, Hanyu Li
openaire   +1 more source

Hyperspectral Image Restoration Using Weighted Group Sparsity-Regularized Low-Rank Tensor Decomposition

IEEE Transactions on Cybernetics, 2020
Mixed noise (such as Gaussian, impulse, stripe, and deadline noises) contamination is a common phenomenon in hyperspectral imagery (HSI), greatly degrading visual quality and affecting subsequent processing accuracy.
Yong Chen, Wei He, N. Yokoya, Tingzhu Huang   +3 more
semanticscholar   +1 more source

Nonlocal Low-Rank Regularized Tensor Decomposition for Hyperspectral Image Denoising

IEEE Transactions on Geoscience and Remote Sensing, 2019
Hyperspectral image (HSI) enjoys great advantages over more traditional image types for various applications due to the extra knowledge available.
Jize Xue, Yongqiang Zhao, Wenzhi Liao, J. Chan   +3 more
semanticscholar   +1 more source

Multi-channel EEG epileptic spike detection by a new method of tensor decomposition

Journal of Neural Engineering, 2020
Objective. Epilepsy is one of the most common brain disorders. For epilepsy diagnosis or treatment, the neurologist needs to observe epileptic spikes from electroencephalography (EEG) data.
Thanh Trung LE, N. Dao, N. Dung, N. Trung, K. Abed-Meraim   +4 more
semanticscholar   +1 more source

A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation

Transportation Research Part C: Emerging Technologies, 2019
The missing data problem is inevitable when collecting traffic data from intelligent transportation systems. Previous studies have shown the advantages of tensor completion-based approaches in solving multi-dimensional data imputation problems.
Xinyu Chen, Zhaocheng He, Lijun Sun
semanticscholar   +1 more source

Tensor Product Decomposition

Proceedings of the International Congress of Mathematicians 2010 (ICM 2010), 2011
Let G be a semisimple connected complex algebraic group. We study the tensor product decomposition of irreducible finite-dimensional representations of G. The techniques we employ range from representation theory to algebraic geometry and topology. This is mainly a survey of author’s various results on the subject obtained individually or jointly with ...
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Nonnegative Tensor Decomposition

2013
It is more and more common to encounter applications where the collected data is most naturally stored or represented in a multi-dimensional array, known as a tensor. The goal is often to approximate this tensor as a sum of some type of combination of basic elements, where the notation of what is a basic element is specific to the type of factorization
N. Hao, L. Horesh, M. E. Kilmer
openaire   +1 more source