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CSDA
2010

Detecting influential observations in Kernel PCA

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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.
Michiel Debruyne, Mia Hubert, Johan Van Horebeek
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where CSDA
Authors Michiel Debruyne, Mia Hubert, Johan Van Horebeek
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