Abstract
We propose a simple use of principal component analysis in feature space that allows the derivation of optimal predictive kernel regression. The proposed approach is shown to perform well on both artificial and real data. Despite its incredible simplicity, the proposed method is found to compete very well with sophisticated statistical approaches like the Relevance Vector Machine and the Support Vector Machine.
Publication Date
2011
Document Type
Article
Department, Program, or Center
The John D. Hromi Center for Quality and Applied Statistics (KGCOE)
Recommended Citation
Fokoue, Ernest, "Optimal predictive kernel regression via feature space principle components" (2011). p. 87-108. Accessed from
https://repository.rit.edu/article/133
Campus
RIT – Main Campus
COinS
