Detecting influential observations in Kernel PCA

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. Key words: Robust statistics, Spherical PCA, kernel methods, influence function.

• Type: Journal
• Year: 2010
• Where: CSDA
• Authors: Michiel Debruyne, Mia Hubert, Johan Van Horebeek
• Keywords: CSDA 2010, Kernel PCA, Spherical Kernel PCA, Spherical PCA