Results 31 to 40 of about 2,392 (180)
On the Tensor SVD and the Optimal Low Rank Orthogonal Approximation of Tensors [PDF]
SIAM Journal on Matrix Analysis and Applications, 2009 It is known that a higher order tensor does not necessarily have an optimal low rank approximation, and that a tensor might not be orthogonally decomposable (i.e., admit a tensor SVD). We provide several sufficient conditions which lead to the failure of the tensor SVD, and characterize the existence of the tensor SVD with respect to the higher order ...
Jie Chen, Yousef Saad
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Вестник рентгенологии и радиологии, 2019 Objective. To compare diffusion-tensor imaging (DTI) measures in different anatomic regions of the brain in patients with an isolated Alzheimer's disease (AD) and patients with AD and small-vessel disease (SVD).Material and methods.
V. A. Perepelov +6 more
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Serum Neurofilament Light Chain Levels Are Related to Small Vessel Disease Burden [PDF]
Journal of Stroke, 2018 Background and Purpose Neurofilament light chain (NfL) is a blood marker for neuroaxonal damage. We assessed the association between serum NfL and cerebral small vessel disease (SVD), which is highly prevalent in elderly individuals and a major cause of ...
Marco Duering +16 more
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Robust Tensor Completion Using Transformed Tensor SVD
, 2019 In this paper, we study robust tensor completion by using transformed tensor singular value decomposition (SVD), which employs unitary transform matrices instead of discrete Fourier transform matrix that is used in the traditional tensor SVD. The main motivation is that a lower tubal rank tensor can be obtained by using other unitary transform matrices
Song, Guangjing +2 more
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Rank revealing‐based tensor completion using improved generalized tensor multi‐rank minimization
IET Signal Processing, 2021 The authors address the problem of tensor completion from limited samplings. An improved generalized tubal Kronecker decomposition is first proposed to reveal the tensor structure of the targeted data, and the improved generalized tensor tubal‐rank and ...
Wei Z. Sun, Peng Zhang, Bo Zhao
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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 +2 more
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Objects features extraction by singular projections of data tensor to matrices
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie ŚrodowiskaThe 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 +3 more
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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 +8 more
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Quantum Algorithms for Tensor-SVD
2024 IEEE International Conference on Quantum Computing and Engineering (QCE)9 pages, 8 ...
Jojo, Jezer +2 more
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