Arrow Diagrams for Kernel Principal Component Analysis Huh, Myung-Hoe;
Kernel principal component analysis(PCA) maps observations in nonlinear feature space to a reduced dimensional plane of principal components. We do not need to specify the feature space explicitly because the procedure uses the kernel trick. In this paper, we propose a graphical scheme to represent variables in the kernel principal component analysis. In addition, we propose an index for individual variables to measure the importance in the principal component plane.
Principal component analysis;kernel method;radial basis function;biplot;arrow diagram;