Results 1 to 10 of about 2,373 (165)
Optimal High-order Tensor SVD via Tensor-Train Orthogonal Iteration. [PDF]
IEEE Trans Inf Theory, 2022 This paper studies a general framework for high-order tensor SVD. We propose a new computationally efficient algorithm, tensor-train orthogonal iteration (TTOI), that aims to estimate the low tensor-train rank structure from the noisy high-order tensor observation.
Zhou Y, Zhang AR, Zheng L, Wang Y.
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Perivascular spaces, diffusion MRI markers and cognitive decline in cerebral small vessel disease [PDF]
Cerebral Circulation - Cognition and BehaviorBackground: MRI markers, including visible perivascular spaces (PVS), diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, and peak width of skeletonized mean diffusivity (PSMD) may capture the earliest pathogenesis of cerebral ...
Gemma Solé-Guardia +11 more
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Unified transformed t-SVD using unfolding tensors for visual inpainting
Computational Visual MediaLow-rank tensor completion (LRTC) restores missing elements in multidimensional visual data; the challenge is representing the inherent structures within this data.
Mengjie Qin +5 more
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Tensor Eigenvalue and SVD from the Viewpoint of Linear Transformation
Axioms, 2023 A linear transformation from vector space to another vector space can be represented as a matrix. This close relationship between the matrix and the linear transformation is helpful for the study of matrices.
Xinzhu Zhao, Bo Dong, Bo Yu, Yan Yu
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On spectral data and tensor decompositions in Finslerian framework [PDF]
AUT Journal of Mathematics and Computing, 2021 The extensions of the Riemannian structure include the Finslerian one, which provided in recent years successful models in various fields like Biology, Physics, GTR, Monolayer Nanotechnology and Geometry of Big Data.
Vladimir Balan
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A Hybrid Norm for Guaranteed Tensor Recovery
Frontiers in Physics, 2022 Benefiting from the superiority of tensor Singular Value Decomposition (t-SVD) in excavating low-rankness in the spectral domain over other tensor decompositions (like Tucker decomposition), t-SVD-based tensor learning has shown promising performance and
Yihao Luo +5 more
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Mathematics, 2021 Face recognition and identification are very important applications in machine learning. Due to the increasing amount of available data, traditional approaches based on matricization and matrix PCA methods can be difficult to implement.
Mustapha Hached +3 more
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Exact Tensor Completion Using t-SVD [PDF]
IEEE Transactions on Signal Processing, 2017 16 pages, 5 figures, 2 ...
Zhang, Zemin, Aeron, Shuchin
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ST-SVD factorization and s-diagonal tensors
Communications in Mathematical Sciences, 2022 A third order real tensor is mapped to a special f-diagonal tensor by going through Discrete Fourier Transform (DFT), standard matrix SVD and inverse DFT. We call such an f-diagonal tensor an s-diagonal tensor. An f-diagonal tensor is an s-diagonal tensor if and only if it is mapped to itself in the above process.
Ling, Chen +3 more
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IET Signal Processing, 2021 Tensor singular value decomposition (t‐SVD) provides a novel way to decompose a tensor. It has been employed mostly in recovering missing tensor entries from the observed tensor entries.
Shuli Ma +4 more
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