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International Journal of Distributed Sensor Networks, 2020 Sparse decomposition has excellent adaptability and high flexibility in describing arbitrary complex signals based on redundant and over-complete dictionary, thus having the advantage of being free from the limitations of traditional signal processing ...
Hongchao Wang, Wenliao Du
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TRUST MODEL FOR SOCIAL NETWORK USING SINGULAR VALUE DECOMPOSITION [PDF]
Interdisciplinary Description of Complex Systems, 2016 For effective interactions to take place in a social network, trust is important. We model trust of agents using the peer to peer reputation ratings in the network that forms a real valued matrix.
Davis Bundi Ntwiga +2 more
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Products, Coproducts, and Singular Value Decomposition [PDF]
International Journal of Theoretical Physics, 2006 17 pages, three eps ...
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The singular value decomposition for polynomial systems
Proceedings of the 1995 international symposium on Symbolic and algebraic computation - ISSAC '95, 1995 This paper introduces singular value decomposition (SVD) algorithms for some standard polynomial computations, in the case where the coecients are inexact or imperfectly known. We first give an algorithm for computing univariate GCD’s which gives exact results for interesting nearby problems, and give ecient algorithms for computing precisely how ...
CORLESS R +3 more
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Zhongguo dianli, 2021 Partial discharge signals are prone to missed detection under low signal-to-noise ratios, and the traditional singular value decomposition algorithm requires massive calculations when extracting partial discharge pulses.
Li WANG, Wei ZHANG, Dingnan LUO
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On the Early History of the Singular Value Decomposition [PDF]
SIAM Review, 1993 The singular value decomposition is the factorization of a matrix \(A\) into the product \(U \sum V^ H\), where \(U\) is a unitary matrix, \(\sum\) a diagonal matrix, and \(V^ H\) another unitary matrix. The author surveys contributions of five mathematicians -- Eugenio Beltrami (1835-1899), Camille Jordan (1838-1921), James Joseph Sylvester (1814-1897)
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Accelerated random noise suppression of seismic data using compressed singular-value decomposition
矿业科学学报Random noise is one of the common background noises in seismic data, and its attenuation will directly affect the signal-to-noise ratio of seismic data, which is of great significance to improve the quality of seismic data.
SUN Chao +4 more
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Split-and-Combine Singular Value Decomposition for Large-Scale Matrix
Journal of Applied Mathematics, 2013 The singular value decomposition (SVD) is a fundamental matrix decomposition in linear algebra. It is widely applied in many modern techniques, for example, high- dimensional data visualization, dimension reduction, data mining, latent semantic analysis,
Jengnan Tzeng
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The Singular Value Decomposition over Completed Idempotent Semifields
Mathematics, 2020 In this paper, we provide a basic technique for Lattice Computing: an analogue of the Singular Value Decomposition for rectangular matrices over complete idempotent semifields (i-SVD).
Francisco J. Valverde-Albacete +1 more
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Identification of Seismic Reflections Using Singular Value Decomposition [PDF]
Modeling, Identification and Control, 1986 Singular value decomposition (SVD) is applied to the identification of seismic reflections by using two different models: the impulse response model where a seismic trace is assumed to consist of a known signal pulse convolved with a reflection ...
Bjørn Ursin, Yuying Zheng
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