Randomized Algorithms for Singular Value Decomposition: Implementation and Application Perspective (CROSBI ID 718321)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Janeković, Darko ; Bojanjac, Dario
engleski
Randomized Algorithms for Singular Value Decomposition: Implementation and Application Perspective
Singular value decomposition (SVD) is a key step in many algorithms in statistics, machine learning and numerical linear algebra. While classical singular value decomposition has been made efficient in terms of computational complexity, classical algorithms are not able to fully utilise modern computing environments. The goal of this work is to survey various implementations and applications of randomized algorithms for SVD. Algorithms are compared in terms of accuracy and time of execution. On example of robust principal component analysis (RPCA), it is shown that using randomized algorithms can yield a significant speedup for image processing and similar applications.
SVD
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Podaci o prilogu
165-168.
2021.
objavljeno
Podaci o matičnoj publikaciji
2021 International Symposium ELMAR
Podaci o skupu
63rd International Symposium ELMAR 2021
predavanje
12.09.2021-15.09.2021
Zadar, Hrvatska