Dictionary-based Tensor Canonical Polyadic Decomposition [PDF]
, 2017 To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary.
Cohen, Jérémy E., Gillis, Nicolas
core +2 more sources
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
doaj +2 more sources
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
doaj +2 more sources
Integrated analysis of human DNA methylation, gene expression, and genomic variation in iMETHYL database using kernel tensor decomposition-based unsupervised feature extraction. [PDF]
PLoS One, 2023 Taguchi YH +5 more
europepmc +2 more sources
Small Defects Detection of Galvanized Strip Steel via Schatten-<i>p</i> Norm-Based Low-Rank Tensor Decomposition. [PDF]
Sensors (Basel)Zhou S, Yan X, Liu H, Gong C.
europepmc +3 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source