Results 21 to 30 of about 31,027 (249)
Singular Value Decomposition [PDF]
The Bulletin of Society for Mathematical Services and Standards, 2014 We study the SVD of an arbitrary matrix Anxm, especially its subspaces of activation, which leads in natural manner to pseudoinverse of Moore-Bjenhammar-Penrose. Besides, we analyze the compatibility of linear systems and the uniqueness of the corresponding solution, and our approach gives the Lanczos classification for these systems.
J.H. Caltenco +3 more
openaire +2 more sources
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
doaj +1 more source
Products, Coproducts, and Singular Value Decomposition [PDF]
International Journal of Theoretical Physics, 2006 17 pages, three eps ...
openaire +3 more sources
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
doaj +1 more source
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)
openaire +1 more source
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
openaire +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
IEEE AccessImage enhancement and reconstruction is an important field of research in digital image analysis. To increase the quality of low-contrast images, a variety of image-enhancing technologies are available.
Shahzada Fahad +7 more
doaj +1 more source