Kernel ridge regression using truncated newton method

摘要

Kernel Ridge Regression (KRR) is a powerful nonlinear regression method. The combination of KRR and the truncated-regularized Newton method, which is based on the conjugate gradient (CG) method, leads to a powerful regression method. The proposed method (algorithm), is called Truncated-Regularized Kernel Ridge Regression (TR-KRR). Compared to the closed-form solution of KRR, Support Vector Machines (SVM) and Least-Squares Support Vector Machines (LS-SVM) algorithms on six data sets, the proposed TR-KRR algorithm is as accurate as, and much faster than all of the other algorithms.