Arrow Diagrams for Kernel Principal Component Analysis

  • Received : 2013.02.06
  • Accepted : 2013.04.08
  • Published : 2013.05.31


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.



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