Results 41 to 50 of about 19,436 (200)

Grassmannian Optimization for Online Tensor Completion and Tracking With the t-SVD

IEEE Transactions on Signal Processing, 2022
We propose a new fast streaming algorithm for the tensor completion problem of imputing missing entries of a low-tubal-rank tensor using the tensor singular value decomposition (t-SVD) algebraic framework. We show the t-SVD is a specialization of the well-studied block-term decomposition for third-order tensors, and we present an algorithm under this ...
Kyle Gilman, Davoud Ataee Tarzanagh, Laura Balzano   +2 more
openaire   +3 more sources

Objects features extraction by singular projections of data tensor to matrices

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
The problem of multidimensional tensor objects features extraction in a manner of matrices is considered. The tensor’ elements Higher Order Singular Value Decomposition (SVD) is presented as the d-SVD which includes SVD of the tensor reshaped as a ...
Yuriy Bunyak, Roman Kvуetnyy, Olga Sofina, Volodymyr Kotsiubynskyi   +3 more
doaj   +1 more source

Quantum Algorithms for Tensor-SVD

2024 IEEE International Conference on Quantum Computing and Engineering (QCE)
9 pages, 8 ...
Jojo, Jezer, Khandelwal, Ankit, Chandra, M Girish   +2 more
openaire   +2 more sources

Multi-Directional Tensor Average Rank Regularization for High-Order Tensor Completion

IEEE Access
Recently, the high-order tensor Singular Value Decomposition (t-SVD) and the t-SVD rank has achieved great success in tensor completion. However, the t-SVD rank lacks the flexibility to capture the correlations between different modes of a high-order ...
Zixuan Han, Mingjian Gu, Yong Hu
doaj   +1 more source

Longitudinal changes in rich club organization and cognition in cerebral small vessel disease

NeuroImage: Clinical, 2019
Cerebral small vessel disease (SVD) is considered the most important vascular contributor to the development of cognitive impairment and dementia. There is increasing awareness that SVD exerts its clinical effects by disrupting white matter connections ...
Esther M.C. van Leijsen, Ingeborg W.M. van Uden, Mayra I. Bergkamp, Helena M. van der Holst, David G. Norris, Jurgen A.H.R. Claassen, Roy P.C. Kessels, Frank-Erik de Leeuw, Anil M. Tuladhar   +8 more
doaj   +1 more source

A Randomized Tensor Train Singular Value Decomposition

, 2017
The hierarchical SVD provides a quasi-best low rank approximation of high dimensional data in the hierarchical Tucker framework. Similar to the SVD for matrices, it provides a fundamental but expensive tool for tensor computations. In the present work we
Huber, Benjamin, Schneider, Reinhold, Wolf, Sebastian   +2 more
core   +1 more source

Modular Matrices as Topological Order Parameter by Gauge Symmetry Preserved Tensor Renormalization Approach [PDF]

, 2014
Topological order has been proposed to go beyond Landau symmetry breaking theory for more than twenty years. But it is still a challenging problem to generally detect it in a generic many-body state.
He, Huan, Moradi, Heidar, Wen, Xiao-Gang
core   +2 more sources

Retinal microvascular dysfunction in mild cognitive impairment: Associations with cerebral small vessel disease, plasma biomarkers, and cognitive decline. [PDF]

Alzheimers Dement
Abstract INTRODUCTION Mild cognitive impairment (MCI) is a transitional stage to dementia, with cerebral small vessel disease (SVD) as a key contributor. Since retinal microvascular changes may mirror cerebral pathology, we investigated the associations between retinal microvasculature, neuroimaging, and plasma biomarkers in individuals with MCI ...
Kwapong WR, Chen Z, Wang Y, Luan X, Chen J, Wen C, Liu Y, Bonaventure IYM, Chen C, Wang Z.   +9 more
europepmc   +2 more sources

Low Tensor Rank Constrained Image Inpainting Using a Novel Arrangement Scheme

Applied Sciences
Employing low tensor rank decomposition in image inpainting has attracted increasing attention. This study exploited novel tensor arrangement schemes to transform an image (a low-order tensor) to a higher-order tensor without changing the total number of
Shuli Ma, Youchen Fan, Shengliang Fang, Weichao Yang, Li Li   +4 more
doaj   +1 more source

T-Hy-Demosaicing: Hyperspectral Reconstruction Via Tensor Subspace Representation Under Orthogonal Transformation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
This article aims to solve the problem of the hyperspectral imagery (HSI) demosaicing under a novel subsampling hyperspectral sensing strategy. The existing method utilizes the periodic structure of subsampling to estimate a fixed subspace in matrix form
Shan-Shan Xu, Ting-Zhu Huang, Jie Lin, Yong Chen   +3 more
doaj   +1 more source