Results 21 to 30 of about 2,509 (175)
Robust Tensor Completion Using Transformed Tensor SVD
CoRR, 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
Guang-Jing Song +2 more
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An Out of Memory tSVD for Big-Data Factorization
IEEE Access, 2020 Singular value decomposition (SVD) is a matrix factorization method widely used for dimension reduction, data analytics, information retrieval, and unsupervised learning.
Hector Carrillo-Cabada +4 more
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Cerebral Circulation - Cognition and Behavior, 2023 Abnormalities in structural and functional MRI connectivity measures have been reported in cerebral small vessel disease (SVD). Previous research has shown that whole-brain structural connectivity was highly reproducible in SVD patients, while whole ...
Daniel J. Tozer +2 more
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Orthogonal random projection for tensor completion
IET Computer Vision, 2020 The low‐rank tensor completion problem, which aims to recover the missing data from partially observable data. However, most of the existing tensor completion algorithms based on Tucker decomposition cannot avoid using singular value decomposition (SVD ...
Yali Feng, Guoxu Zhou
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NeuroImage: Clinical, 2021 Cerebral small vessel disease (SVD), including white matter hyperintensities (WMH), microbleeds, lacunes, was related to gait disturbances, while the underlying mechanism is unclear.
Mengfei Cai +5 more
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Triad second renormalization group
Journal of High Energy Physics, 2022 We propose a second renormalization group (SRG) in the triad representation of tensor networks. The SRG method improves two parts of the triad tensor renormalization group, which are the decomposition of intermediate tensors and the preparation of ...
Daisuke Kadoh +2 more
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Tensor SVD and distributed control [PDF]
SPIE Proceedings, 2005 The (approximate) diagonalization of symmetric matrices has been studied in the past in the context of distributed control of an array of collocated smart actuators and sensors. For distributed control using a two dimensional array of actuators and sensors, it is more natural to describe the system transfer function as a complex tensor rather than a ...
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An Aggregative High-Order Singular Value Decomposition Method in Edge Computing
IEEE Access, 2020 In edge computing, for dimensionality reduction and core data extraction, both edge computing node (ECN) and cloud server may implement a high-order singular value decomposition (HOSVD) algorithm before data are passed to local computing models. However,
Junhua Chen +3 more
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T-SVD-Based Robust Color Image Watermarking
IEEE Access, 2019 In order to protect the copyright of the color image, a robust color image watermarking method based on Tensor-Singular Value Decomposition (T-SVD) is proposed.
Meng Du +4 more
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A Tensor SVD-based Classification Algorithm Applied to fMRI Data
SIAM Undergraduate Research Online, 2022 To analyze the abundance of multidimensional data, tensor-based frameworks have been developed. Traditionally, the matrix singular value decomposition (SVD) is used to extract the most dominant features from a matrix containing the vectorized data. While the SVD is highly useful for data that can be appropriately represented as a matrix, this step of ...
Katherine Keegan +2 more
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