Exploring the feasibility of tensor decomposition for analysis of fNIRS signals: a comparative study with grand averaging method [PDF]
Frontiers in Neuroscience, 2023 The analysis of functional near-infrared spectroscopy (fNIRS) signals has not kept pace with the increased use of fNIRS in the behavioral and brain sciences.
Jasmine Y. Chan +4 more
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Tensor decomposition of transportation temporal and spatial big data: A brief review [PDF]
Fundamental ResearchRecent development in sensing and communication technologies has made the collection of a large amount of traffic data easy and transportation engineering has entered the big data era.
Linchao Li, Xiang Lin, Bin Ran, Bowen Du
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Fast Circulant Tensor Power Method for High-Order Principal Component Analysis
IEEE Access, 2021 To understand high-order intrinsic key patterns in high-dimensional data, tensor decomposition is a more versatile tool for data analysis than standard flat-view matrix models. Several existing tensor models aim to achieve rapid computation of high-order
Taehyeon Kim, Yoonsik Choe
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Block Row Kronecker-Structured Linear Systems With a Low-Rank Tensor Solution
Frontiers in Applied Mathematics and Statistics, 2022 Several problems in compressed sensing and randomized tensor decomposition can be formulated as a structured linear system with a constrained tensor as the solution.
Stijn Hendrikx +3 more
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N Dimensional Tensor Decomposition Recommendation Algorithm Based on User’s Neighbors [PDF]
Jisuanji gongcheng, 2017 Recommendation algorithm based on tensor factorization has low accuracy and data sparseness problem.Therefore,on the basic of the traditional tensor decomposition model,this paper introduces the user nearest neighbor information,and proposes N ...
CHEN Jianmei,SUN Yajun
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Tensor Decomposition-Inspired Convolutional Autoencoders for Hyperspectral Anomaly Detection
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Anomaly detection from hyperspectral images (HSI) is an important task in the remote sensing domain. Considering the three-order characteristics of HSI, many tensor decomposition based hyperspectral anomaly detection (HAD) models have been proposed and ...
Bangyong Sun +4 more
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EA-ADMM: noisy tensor PARAFAC decomposition based on element-wise average ADMM
EURASIP Journal on Advances in Signal Processing, 2022 Tensor decomposition is widely used to exploit the internal correlation in multi-way data analysis and process for communications and radar systems.
Gang Yue, Zhuo Sun
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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
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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
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上海师范大学学报. 自然科学版, 2021 By using the sparse scattering characteristics of the millimeter wave channel and the spatial structure of the tensor, a channel estimation method based on random grid tensor decomposition was proposed.
ZHANG Jing +3 more
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