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Kernel Principal Component Analysis: Applications, implementation and comparison
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2013 (English)In: Models, Algorithms, and Technologies for Network Analysis: Proceedings of the Second International Conference on Network Analysis, Springer, 2013, 127-147 p.Conference paper, Published paper (Refereed)
Abstract [en]

Kernel Principal Component Analysis (KPCA) is a dimension reduction method that is closely related to Principal Component Analysis (PCA). This report gives an overview of kernel PCA and presents an implementation of the method in MATLAB. The implemented method is tested in a transductive setting on two data bases. Two methods for labeling data points are considered, the nearest neighbor method and kernel regression, together with some possible improvements of the methods.

Place, publisher, year, edition, pages
Springer, 2013. 127-147 p.
Series
Springer Proceedings in Mathematics and Statistics, ISSN 2194-1009 ; 59
Keyword [en]
Dimension reduction, Kernel principal component analysis, Kernel regression, Nearest neighbor, Reproducing kernel Hilbert space
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-143135DOI: 10.1007/978-1-4614-8588-9_9Scopus ID: 2-s2.0-84893463678ISBN: 978-146148587-2 (print)OAI: oai:DiVA.org:kth-143135DiVA: diva2:705880
Conference
2nd International Conference on Network Analysis; Nizhny Novgorod; Russian Federation; 7 May 2012 through 9 May 2012
Note

QC 20140318

Available from: 2014-03-18 Created: 2014-03-17 Last updated: 2014-03-18Bibliographically approved

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