Results 41 to 50 of about 1,077,071 (292)
Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data [PDF]
Technometrics, 2023 We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets.
Jiuyun Hu +3 more
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Measurement Science Review, 2020 Analysis of changes in the brain neural electrical activity measured by the electroencephalogram (EEG) plays a crucial role in the area of brain disorder diagnostics. The elementary latent sources of the brain neural activity can be extracted by a tensor
Rošt’áková Zuzana +3 more
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EURASIP Journal on Advances in Signal Processing, 2023 Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar is an emerging technology to offer range-angle-dependent beampattern. Polarimetric FDA-MIMO radar can sense additional polarization information to improve target identification ...
Qi Zhang, Hong Jiang, Yunchang Liu
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Faster Quantum State Decomposition with Tucker Tensor Approximation
Quantum Machine Intelligence, 2022 Abstract Researchers have put a lot of effort into reducing the gap between current quantum processing units (QPU) capabilities and their potential supremacy.One approach is to keep supplementary computations in the CPU, and use QPU only for the core of the problem.
Protasov Stanislav, Lisnichenko Marina
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Nonnegative Tensor Completion via Low-Rank Tucker Decomposition: Model and Algorithm
IEEE Access, 2019 We consider the problem of low-rank tensor decomposition of incomplete tensors that has applications in many data analysis problems, such as recommender systems, signal processing, machine learning, and image inpainting.
Bilian Chen +4 more
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Tensor dictionary learning with sparse TUCKER decomposition
2013 18th International Conference on Digital Signal Processing (DSP), 2013 Dictionary learning algorithms are typically derived for dealing with one or two dimensional signals using vector-matrix operations. Little attention has been paid to the problem of dictionary learning over high dimensional tensor data. We propose a new algorithm for dictionary learning based on tensor factorization using a TUCKER model.
Zubair, S, Wang, W
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Point Cloud Denoising Based on Tensor Tucker Decomposition [PDF]
2019 IEEE International Conference on Image Processing (ICIP), 2019 5 pages, 1 ...
Li, Jianze, Zhang, Xiao-Ping, Tran, Tuan
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IEEE Access, 2019 In order to deal with the problem of massive historical multi-station SCADA data storage in distribution management system, this paper proposes a data compression method for power distribution system based on tensor Tucker decomposition.
Hongshan Zhao +3 more
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Randomized Algorithms for Computation of Tucker Decomposition and Higher Order SVD (HOSVD)
IEEE Access, 2021 Big data analysis has become a crucial part of new emerging technologies such as the internet of things, cyber-physical analysis, deep learning, anomaly detection, etc.
Salman Ahmadi-Asl +6 more
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Noisy Non‐Negative Tucker Decomposition With Sparse Factors and Missing Data
Numerical Linear Algebra with ApplicationsTensor decomposition is a powerful tool for extracting physically meaningful latent factors from multi‐dimensional non‐negative data, and has been an increasing interest in a variety of fields such as image processing, machine learning, and computer ...
Xiongjun Zhang, Michael K. Ng
semanticscholar +1 more source