Publication
Title
Detecting influential observations in Kernel PCA
Author
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.
Language
English
Source (journal)
Computational statistics and data analysis. - Amsterdam
Publication
Amsterdam : 2010
ISSN
0167-9473
Volume/pages
54:12(2010), p. 3007-3019
ISI
000281333900011
Full text (Publishers DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 20.10.2010
Last edited 07.04.2017
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