1. Kernel partial least squares for nonlinear regression and discrimination
- Creator:
- Rosipal, Roman
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- kernel-based learning, partial least squares, and support vector machines
- Language:
- English
- Description:
- 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.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public