Application of Kernel Principal Components Analysis to pattern recognitions

Kernel Principal Component Analysis (Kernel PCA) is one of the methods to perform PCA in high dimensional space. The purpose of this paper is to examine what components are obtained by Kernel PCA and evaluate effectiveness of the components as feature. Simulation's results show that Kernel PCA can get superior performance to PCA.