Work

Kernel Partial Least Squares for Nonlinear Regression and Discrimination

Year: 2002

Type: article

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 kernel PLS regression model was proven to be competitive with other regularized regression methods in RKHS. The family of nonlinear kernel-based PLS models is extended by considering the kernel PLS method for discrimination. Theoretical and experimental results on a two-class discrimination problem indicate usefulness of the method.

Source: NASA STI Repository (National Aeronautics and Space Administration)

Authors: Roman Rosipal, Daniel Clancy

Institution: Ames Research Center

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W807996015

FWCI: 1.047

Cites: 16

Cited by: 98

Related to: 10