Results 1 to 10 of about 2,604 (146)
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.
Xu Honghui, Minhong Wan, Chunlong Zhang
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Optimal High-Order Tensor SVD via Tensor-Train Orthogonal Iteration [PDF]
IEEE Transactions on Information Theory, 2022 Anru Zhang, Lili Zheng, Yuchen Zhou
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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
doaj +1 more source
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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DTI-ALPS as a Biomarker of Small Vessel Disease Progression and Amyloid-β in Normal Aging: A 3-Year Longitudinal Study. [PDF]
Hum Brain MappA higher DTI‐derived ALPS index relates cross‐sectionally to both quantitative and visual measures of SVD and is associated with short‐term amyloid‐β accumulation in cognitively normal older adults. Also, greater amyloid burden and choroid plexus volume are associated with a more rapid deterioration in global cognition and processing speed.
Azizi N +6 more
europepmc +2 more sources
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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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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Online Tensor Robust Principal Component Analysis
IEEE Access, 2022 Online robust principal component analysis (RPCA) algorithms recursively decompose incoming data into low-rank and sparse components. However, they operate on data vectors and cannot directly be applied to higher-order data arrays (e.g. video frames). In
Mohammad M. Salut, David V. Anderson
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Cross Tensor Approximation Methods for Compression and Dimensionality Reduction
IEEE Access, 2021 Cross Tensor Approximation (CTA) is a generalization of Cross/skeleton matrix and CUR Matrix Approximation (CMA) and is a suitable tool for fast low-rank tensor approximation.
Salman Ahmadi-Asl +6 more
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A Deep Nonlinear Subspace Modeling and Reconstruction for Diffusion-Weighted Imaging Using Denoising Auto-Encoder. [PDF]
Magn Reson MedABSTRACT Purpose To present a novel, nonlinear subspace modeling and joint k–q‐space reconstruction technique for high‐resolution, multi‐band, multi‐shell diffusion‐weighted imaging (DWI). Methods High b‐value (> 1000 s/mm2), high resolution DWI has the drawback of generally low signal‐to‐noise ratios (SNRs).
Glaser J +4 more
europepmc +2 more sources