Abstract
This paper summarizes recent results on applying the method of partial least squares (PLS)
in a reproducing kernel Hilbert space (RKHS).
A previously proposed nonlinear kernel-based PLS regression model has proven to be competitive with other regularized regression methods
in RKHS. In this paper the use of kernel PLS for discrimination is discussed.
A new methodology for classification is then proposed. This is based on kernel PLS dimensionality reduction
of the original data space followed by a support vector classifier.
Good results using this method on a two-class classification problem are reported here.
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