Robust, randomized preconditioning for kernel ridge regression

• Published in: arXiv.org
• Main Authors: Díaz, Mateo, Epperly, Ethan N, Frangella, Zachary, Tropp, Joel A, Webber, Robert J
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 10.07.2024
• Subjects: Data centers; Data points; Eigenvalues; Failure modes; Kernels; Krill; Polynomials; Preconditioning; Robustness (mathematics)

This paper investigates two randomized preconditioning techniques for solving kernel ridge regression (KRR) problems with a medium to large number of data points (\(10^4 \leq N \leq 10^7\)), and it introduces two new methods with state-of-the-art performance. The first method, RPCholesky preconditio...