Title
Detecting influential observations in Kernel PCADetecting influential observations in Kernel PCA
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Research group
Department of Mathematics - Computer Sciences
Publication type
article
Publication
Amsterdam,
Subject
Computer. Automation
Source (journal)
Computational statistics and data analysis. - Amsterdam
Volume/pages
54(2010):12, p. 3007-3019
ISSN
0167-9473
ISI
000281333900011
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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.
E-info
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