On kernel principal component analysis

Abstract

When dealing with multivariate data with higher dimensions, we often use principal component analysis (pca) to reduce the dimensions, but in the case of nonlinear data it is not possible to deal with classic estimated because of obtaining misleading results and therefore using kernel methods , The aim of this research is used KPCA to solve nonlinear data using kernel function  to know the most effect variables on the phenomenon.