Robust principal component analysis for functional data

A method for exploring the structure of populations of complex objects, such as images, is considered. The objects are summarized by feature vectors. The statistical backbone is Principal Component Analysis in the space of feature vectors. Visual insights come from representing the results in the or...

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Veröffentlicht in:	Test (Madrid, Spain) Spain), 1999-06, Vol.8 (1), p.1-73
Hauptverfasser:	Locantore, N., Marron, J. S., Simpson, D. G., Tripoli, N., Zhang, J. T., Cohen, K. L., Boente, Graciela, Fraiman, Ricardo, Brumback, Babette, Croux, Christophe, Fan, Jianqing, Kneip, Alois, Marden, John I., Peña, Daniel, Prieto, Javier, Ramsay, Jim O., Valderrama, Mariano J., Aguilera, Ana M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Principal components analysis Studies
Online-Zugang:	Volltext
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Beschreibung
Zusammenfassung:	A method for exploring the structure of populations of complex objects, such as images, is considered. The objects are summarized by feature vectors. The statistical backbone is Principal Component Analysis in the space of feature vectors. Visual insights come from representing the results in the original data space. In an ophthalmological example, endemic outliers motivate the development of a bounded influence approach to PCA.[PUBLICATION ABSTRACT]
ISSN:	1133-0686 1863-8260
DOI:	10.1007/BF02595862