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ICML
2003
IEEE

Kernel PLS-SVC for Linear and Nonlinear Classification

11 years 3 months ago
Kernel PLS-SVC for Linear and Nonlinear Classification
A new method for classification is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by a support vector classifier. Unlike principal component analysis (PCA), which has previously served as a dimension reduction step for discrimination problems, orthonormalized PLS is closely related to Fisher's approach to linear discrimination or equivalently to canonical correlation analysis. For this reason orthonormalized PLS is preferable to PCA for discrimination. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of classifying finger movement periods from non-movement periods based on electroencephalograms.
Roman Rosipal, Leonard J. Trejo, Bryan Matthews
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2003
Where ICML
Authors Roman Rosipal, Leonard J. Trejo, Bryan Matthews
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