Detecting influential observations in Kernel PCA

Michiel Debruyne, Mia Hubert and Johan Van Horebeek

Computational Statistics & Data Analysis, 2010, vol. 54, issue 12, 3007-3019

Abstract: Kernel Principal Component Analysis extends linear PCA from a Euclidean space to any reproducing kernel Hilbert space. Robustness issues for Kernel PCA are studied. The sensitivity of Kernel PCA to individual observations is characterized by calculating the influence function. A robust Kernel PCA method is proposed by incorporating kernels in the Spherical PCA algorithm. Using the scores from Spherical Kernel PCA, a graphical diagnostic is proposed to detect points that are influential for ordinary Kernel PCA.

Date: 2010
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