Boosting Stability and Performance in Randomized SVD
Journal
AIP Conference Proceedings
Journal Volume
3315
Start Page
100003
ISSN
0094243X
ISBN (of the container)
9780735452459
ISBN
9780735452459
Date Issued
2025-09-11
Author(s)
Abstract
Randomized singular value decomposition (rSVD) is a highly efficient technique for approximating the singular value decomposition of a resultant matrix using a randomized sampling methods, as opposed to traditional SVD algorithms. This study presents the truncated singular value decomposition (trSVD), specifically tailored for Kernel-Based Methods such as radial basis function (RBF) ones. This novel approach significantly enhances stability and reduces the condition number of the linear system. Our experimental results demonstrate the effectiveness of the truncation process in trSVD. Furthermore, we thoroughly evaluate the performance of trSVD across Gaussian and Multiquadric RBFs. Truncated randomized SVD improves stability and accuracy in kernel-based methods with promising results in diverse RBF settings.
Event(s)
2023 International Conference on Numerical Analysis and Applied Mathematics, ICNAAM 2023, Heraklion, 11 September 2023 - 17 September 2023
SDGs
Publisher
American Institute of Physics
Type
conference paper