Kernel Principal Component Regression in Reproducing Kernel Hilbert Space
Chooleewan Dachapak, Shunshoku Kanae, Zi-Jiang Yang, Kiyoshi Wada · Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications · 2003
In this study, we proposed Kernel Principal Component Analysis (KPCA) which is applied for feature selection in a high-dimensional feature space which is nonlinearly mapped from an input space by a Gaussian kernel function. By using Mercer Kernels, we can compute principal components in a high dimensional feature space. Then, the extracted features are employed as preprocessing step for an ordinary least squares regression in the feature space which is Reproducing Kernel Hilbert Space (RKHS).