Results 31 to 40 of about 223,331 (198)
IEEE Access, 2021 If the three-dimension data of batch process are unfolded the two-dimension data, some important information would lose, and outliers such as noise would lead to poor monitoring results.
Xiaoqiang Zhao, Miao Mou
doaj +1 more source
PLoS ONE, 2014 The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding
Laetitia Gauvin +2 more
doaj +1 more source
Sparsity-Constrained Coupled Nonnegative Matrix–Tensor Factorization for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Hyperspectral unmixing refers to a source separation problem of decomposing a hyperspectral imagery (HSI) to estimate endmembers, and their corresponding abundances.
Heng-Chao Li +3 more
doaj +1 more source
The q-Onsager algebra and its alternating central extension
Nuclear Physics B, 2022 The q-Onsager algebra Oq has a presentation involving two generators W0, W1 and two relations, called the q-Dolan/Grady relations. The alternating central extension Oq has a presentation involving the alternating generators {W−k}k=0∞, {Wk+1}k=0∞, {Gk+1}k=
Paul Terwilliger
doaj +1 more source
B-Tensor: Brain Connectome Tensor Factorization for Alzheimer's Disease
IEEE Journal of Biomedical and Health Informatics, 2021 AD is the highly severe part of the dementia spectrum and impairs cognitive abilities of individuals, bringing economic, societal and psychological burdens beyond the diseased. A promising approach in AD research is the analysis of structural and functional brain connectomes, i.e., sNETs and fNETs, respectively. We propose to use tensor representation (
Göktekin Durusoy +13 more
openaire +6 more sources
Parallel tensor factorization for relational learning [PDF]
, 2021 Link prediction is a statistical relational learning problem that has a variety of applications in recommender systems, expert systems, and knowledge bases. Numerous approaches have already been devised to solve the problem.
Al-Obeidat, Feras +4 more
core +1 more source
Tensor Factorization via Matrix Factorization
CoRR, 2015 Tensor factorization arises in many machine learning applications, such knowledge base modeling and parameter estimation in latent variable models. However, numerical methods for tensor factorization have not reached the level of maturity of matrix factorization methods.
Volodymyr Kuleshov +2 more
openaire +3 more sources
Realization of tensor product and of tensor factorization of rational functions [PDF]
Quantum Studies: Mathematics and Foundations, 2019 We here first study the state space realization of a tensor-product of a pair of rational functions. At the expense of "inflating" the dimensions, we recover the classical expressions for realization of a regular product of rational functions. Then, under an additional assumption that the limit at infinity of a given rational function exists and is ...
Daniel Alpay, Izchak Lewkowicz
openaire +3 more sources
Spline- and tensor-based signal reconstruction : from structure analysis to high-performance algorithms to multiplatform implementations and medical applications [PDF]
, 2015 The problem of signal reconstruction is of fundamental practical value for many applications associated with the field of signal and image processing.
Morozov, Oleksii
core +1 more source
Apply Optimized Tensor Completion Method by Bayesian CP-Factorization for Image Recovery [PDF]
Control and Optimization in Applied Mathematics, 2021 In this paper, we are going to analyze big data (embedded in the digital images) with new methods of tensor completion (TC). The determination of tensor ranks and the type of decomposition are significant and essential matters.
Ali Reza Shojaeifard +2 more
doaj +1 more source