Optimal Learning Rates for Kernel Partial Least Squares

Abstract

We study two learning algorithms generated by kernel partial least squares (KPLS) and kernel minimal residual (KMR) methods. In these algorithms, regularization against overfitting is obtained by early stopping, which makes stopping rules crucial to their learning capabilities. We propose a stopping rule for determining the number of iterations based on cross-validation, without assuming a priori knowledge of the underlying probability measure, and show that optimal learning rates can be achieved. Our novel analysis consists of a nice bound for the number of iterations in a priori knowledge-based stopping rule for KMR and a stepping stone from KMR to KPLS. Technical tools include a recently developed integral operator approach based on a second order decomposition of inverse operators and an orthogonal polynomial argument.