Results 11 to 20 of about 2,509 (175)
Association of imaging-defined brain age with disease severity and adverse outcomes in CADASIL. [PDF]
Alzheimers DementAbstract INTRODUCTION Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is caused by cysteine‐altering NOTCH3 variants. We examined whether neuroimaging‐defined brain age is altered in CADASIL and its association with disease severity and outcomes.
Hsu SL +7 more
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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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A tensor SVD-like decomposition based on the semi-tensor product of tensors
CoRR, 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.
Zhuo-Ran Chen +2 more
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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.
Chen Ling 0001 +3 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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Adaptive Multilinear SVD for Structured Tensors [PDF]
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006 The higher-order SVD (HOSVD) is a generalization of the SVD to higher-order tensors (ie. arrays with more than two indexes) and plays an important role in various domains. Unfortunately, the computational cost of this decomposition is very high since the basic HOSVD algorithm involves the computation of the SVD of three highly redundant block-Hankel ...
Rémy Boyer, Roland Badeau
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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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Sensors, 2022 A fast inversion algorithm combined with the transient electromagnetic (TEM) detection system has important significance for improving the detection efficiency of unexploded ordnance.
Lijie Wang +3 more
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