Results 21 to 30 of about 19,436 (200)
Very Large-Scale Singular Value Decomposition Using Tensor Train Networks [PDF]
, 2014 We propose new algorithms for singular value decomposition (SVD) of very large-scale matrices based on a low-rank tensor approximation technique called the tensor train (TT) format. The proposed algorithms can compute several dominant singular values and
Cichocki, Andrzej, Lee, Namgil
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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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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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A Constructive Algorithm for Decomposing a Tensor into a Finite Sum of Orthonormal Rank-1 Terms [PDF]
, 2015 We propose a constructive algorithm that decomposes an arbitrary real tensor into a finite sum of orthonormal rank-1 outer products. The algorithm, named TTr1SVD, works by converting the tensor into a tensor-train rank-1 (TTr1) series via the singular ...
Batselier, Kim, Liu, Haotian, Wong, Ngai
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A tensor SVD-like decomposition based on the semi-tensor product of tensors
, 2023 In this paper, we define a semi-tensor product for third-order tensors. Based on this definition, we present a new type of tensor decomposition strategy and give the specific algorithm. This decomposition strategy actually generalizes the tensor SVD based on semi-tensor product.
Chen, Zhuo-Ran +2 more
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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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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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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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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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Generic Construction of Efficient Matrix Product Operators [PDF]
, 2017 Matrix Product Operators (MPOs) are at the heart of the second-generation Density Matrix Renormalisation Group (DMRG) algorithm formulated in Matrix Product State language.
Hubig, C. +2 more
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