Kernel PLS variants for regression

Abstract. We focus on covariance criteria for finding a suitable subspace for regression in a reproducing kernel Hilbert space: kernel principal component analysis, kernel partial least squares and kernel canonical correlation analysis, and we demonstrate how this fits within a more general context of subspace regression. For the kernel partial least squares case some variants are considered and the methods are illustrated and compared on a number of examples.

• Type: Conference
• Year: 2003
• Where: ESANN
• Authors: Luc Hoegaerts, Johan A. K. Suykens, Joos Vandewalle, Bart De Moor
• Keywords: ESANN 2003, ESANN 2007, Kernel Canonical Correlation, Kernel Principal Component Analysis, Reproducing Kernel Hilbert Space