Results 21 to 30 of about 6,697,803 (274)

Privacy-Preserving Federated Singular Value Decomposition

Applied Sciences, 2023
Singular value decomposition (SVD) is a fundamental technique widely used in various applications, such as recommendation systems and principal component analyses.
Bowen Liu, Balázs Pejó, Qiang Tang
doaj   +1 more source

Singular Value Decomposition of Spatial Matrices

Современные информационные технологии и IT-образование, 2022
Singular value decomposition is a basic building block which is used in solution of many different problems. In cases when dimensionality of a problem exceeds two, a generalization of a singular value decomposition – tensor decompositions – are used ...
Pavel Iljin, Tatiana Samoilova
doaj   +1 more source

Singular Value Decomposition Quantum Algorithm for Quantum Biology [PDF]

ACS Physical Chemistry Au
Emily K. Oh, Timothy J. Krogmeier, Anthony W. Schlimgen, Kade Head-Marsden   +3 more
doaj   +2 more sources

Singular Value Decomposition of Complexes [PDF]

SIAM Journal on Applied Algebra and Geometry, 2019
Singular value decompositions of matrices are widely used in numerical linear algebra with many applications. In this paper, we extend the notion of singular value decompositions to finite complexes of real vector spaces. We provide two methods to compute them and present several applications.
Danielle A. Brake, Jonathan D. Hauenstein, Frank-Olaf Schreyer, Andrew J. Sommese, Michael Eugene Stillman   +4 more
openaire   +4 more sources

Watermark Based on Singular Value Decomposition

مجلة بغداد للعلوم, 2023
Watermarking operation can be defined as a process of embedding special wanted and reversible information in important secure files to protect the ownership or information of the wanted cover file based on the proposed singular value decomposition (SVD)
Ali Abdulazeez Mohammed Baqer Qazzaz, Neamah Enad Kadhim   +1 more
doaj   +1 more source

A generalization of the randomized singular value decomposition

CoRR, 2021
The randomized singular value decomposition (SVD) is a popular and effective algorithm for computing a near-best rank $k$ approximation of a matrix $A$ using matrix-vector products with standard Gaussian vectors. Here, we generalize the randomized SVD to multivariate Gaussian vectors, allowing one to incorporate prior knowledge of $A$ into the ...
Boulle, N, Townsend, A
openaire   +7 more sources

A Novel Strategy for Signal Denoising Using Two-Layer SVD and Its Application to Rub-Impact Fault Diagnosis of Aeroengine

Applied Sciences, 2022
The rub-impact fault, which occurs in the aeroengine, can cause frequency modulation (FM) in its vibration signal. Oscillated instantaneous frequency (IF) is a phenomenon of such a modulation mode, which carries critical fault information from the ...
Peng Zhang, Shunming Li, Kun Xu, Siqi Gong, Keyi Zhan, Maoxian Shen, Jun Tian, Xin Yuan   +7 more
doaj   +1 more source

Truncated singular value decomposition in ripped photo recovery [PDF]

ITM Web of Conferences, 2021
Singular value decomposition (SVD) is one of the most useful matrix decompositions in linear algebra. Here, a novel application of SVD in recovering ripped photos was exploited. Recovery was done by applying truncated SVD iteratively.
Lem Kong Hoong
doaj   +1 more source

Scalability of k-Tridiagonal Matrix Singular Value Decomposition

Mathematics, 2021
Singular value decomposition has recently seen a great theoretical improvement for k-tridiagonal matrices, obtaining a considerable speed up over all previous implementations, but at the cost of not ordering the singular values.
Andrei Tănăsescu, Mihai Carabaş, Florin Pop, Pantelimon George Popescu   +3 more
doaj   +1 more source

Partial Singular Value Assignment for Large-Scale Systems

Axioms, 2023
The partial singular value assignment problem stems from the development of observers for discrete-time descriptor systems and the resolution of ordinary differential equations.
Yiting Huang, Qiong Tang, Bo Yu
doaj   +1 more source