Title
Robust kernel principal component analysis and classification Robust kernel principal component analysis and classification
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
Subject
Mathematics
Source (journal)
Advances in Data Analysis and Classification
Volume/pages
4(2010) :2-3 , p. 151-167
ISSN
1862-5347
ISI
000284145800005
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Kernel principal component analysis (KPCA) extends linear PCA from a real vector space to any high dimensional kernel feature space. The sensitivity of linear PCA to outliers is well-known and various robust alternatives have been proposed in the literature. For KPCA such robust versions received considerably less attention. In this article we present kernel versions of three robust PCA algorithms: spherical PCA, projection pursuit and ROBPCA. These robust KPCA algorithms are analyzed in a classification context applying discriminant analysis on the KPCA scores. The performances of the different robust KPCA algorithms are studied in a simulation study comparing misclassification percentages, both on clean and contaminated data. An outlier map is constructed to visualize outliers in such classification problems. A real life example from protein classification illustrates the usefulness of robust KPCA and its corresponding outlier map.
E-info
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000284145800005&DestLinkType=RelatedRecords&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000284145800005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000284145800005&DestLinkType=CitingArticles&DestApp=ALL_WOS&UsrCustomerID=ef845e08c439e550330acc77c7d2d848
Handle