A Fast Cross-Validation Algorithm for Kernel Ridge Regression by Eigenvalue Decomposition

Akira TANAKA  Hideyuki IMAI  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E102-A   No.9   pp.1317-1320
Publication Date: 2019/09/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E102.A.1317
Type of Manuscript: LETTER
Category: Numerical Analysis and Optimization
Keyword: 
kernel ridge regression,  model selection,  hyperparameter,  cross-validation,  

Full Text: PDF(260.7KB)>>
Buy this Article



Summary: 
A fast cross-validation algorithm for model selection in kernel ridge regression problems is proposed, which is aiming to further reduce the computational cost of the algorithm proposed by An et al. by eigenvalue decomposition of a Gram matrix.