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Title
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Detecting influential observations in Kernel PCA
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Author
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Abstract
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| 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. |
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Language
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English
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Source (journal)
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Computational statistics and data analysis / International Association for Statistical Computing. - Amsterdam, 1983, currens
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Publication
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Amsterdam
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North-Holland
,
2010
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ISSN
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0167-9473
[print]
1872-7352
[online]
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Volume/pages
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54
:12
(2010)
, p. 3007-3019
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ISI
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000281333900011
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Full text (Publisher's DOI)
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