R. Rosipal and L.J. Trejo (2001)

# Kernel Partial Least Squares Regression in RKHS

CIS Department, University of Paisley, UK.

A family of regularized least squares regression models in a Reproducing Kernel Hilbert Space is extended by the Kernel Partial Least Squares (PLS) regression model. Similar to Principal Components Regression (PCR), PLS is a method based on the projection of input (explanatory) variables to the latent variables (components). However, in contrast to PCR, PLS creates the components by modeling the relationship between input and output variables while maintaining most of the input variables information. PLS is considered to be useful in situations where the number of explanatory variables exceeds the number of observations and/or a high level of multicollinearity among those variables is assumed. Motivated by this fact we will provide a Kernel PLS algorithm for construction of non-linear regression models in possibly high-dimensional feature spaces.