Results 11 to 20 of about 6,697,803 (274)
Singular value decomposition-based gait characterization [PDF]
Heliyon, 2022 Background: Human gait varies based on personal characteristics, the existence of walking problems, or variability of gait parameters. Identifying the sources of variations is significant in detecting walking problems, designing orthotic/prosthetic ...
Cem Guzelbulut +2 more
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Memristive singular value decomposition [PDF]
Nature CommunicationsSingular value decomposition (SVD) underpins low-rank representation across scientific computing, signal processing, and machine learning. However, iterative computations in SVD are energy-intensive on conventional von Neumann architectures with separate
Chenchen Ding +8 more
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Variational Quantum Singular Value Decomposition [PDF]
Quantum, 2021 Singular value decomposition is central to many problems in engineering and scientific fields. Several quantum algorithms have been proposed to determine the singular values and their associated singular vectors of a given matrix.
Xin Wang, Zhixin Song, Youle Wang
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Singular-Value-Decomposition-Based Matrix Surgery [PDF]
EntropyThis paper is motivated by the need to stabilise the impact of deep learning (DL) training for medical image analysis on the conditioning of convolution filters in relation to model overfitting and robustness.
Jehan Ghafuri, Sabah Jassim
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Differential privacy protection algorithm for network sensitive information based on singular value decomposition [PDF]
Scientific Reports, 2023 In order to reduce the risk of data privacy disclosure and improve the effect of information privacy protection, a differential privacy protection algorithm for network sensitive information based on singular value decomposition is proposed.
Xuan Ma, Xing Chang, Hongxiu Chen
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Hybrid Threshold Denoising Framework Using Singular Value Decomposition for Side-Channel Analysis Preprocessing [PDF]
Entropy, 2023 The traces used in side-channel analysis are essential to breaking the key of encryption and the signal quality greatly affects the correct rate of key guessing.
Yuanzhen Wang +7 more
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Theoretical and Natural ScienceSingular Value Decomposition (SVD) is a very important matrix factorization technique in linear algebra which generalizes the eigenvalue decomposition to both non square and non symmetric matrices. This report explains the theoretical foundation of SVD by numerical examples and the comparison of SVD with eigenvalue decomposition on the basis of ...
Peter Zizler, Roberta La Haye
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, 2014 The singular value decomposition is a factorization of a matrix A into A=U.EV^TComponente Curricular::Educação Superior::Ciências Exatas e da Terra ...
Maes, Chris
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, 2011 While the eigenvalue decomposition \({\bf A} = \bf T{\bf \Lambda}T^{\prime},\) say, concerns only symmetric matrices, the singular value decomposition (SVD) \({\bf A} = \bf U{\bf \Delta}V^{\prime},\) say, concerns any n × m matrix. In this chapter we illustrate the usefulness of the SVD, particularly from the statistical point of view.
Simo Puntanen +2 more
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N-Dimensional Principal Component Analysis [PDF]
, 2010 In this paper, we first briefly introduce the multidimensional Principal Component Analysis (PCA) techniques, and then amend our previous N-dimensional PCA (ND-PCA) scheme by introducing multidirectional decomposition into ND-PCA implementation.
Yu, Hongchuan
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