Cross-validation methods in principal component analysis: A comparison

Giancarlo Diana () and Chiara Tommasi ()
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Giancarlo Diana: Università di Padova
Chiara Tommasi: Università di Padova

Statistical Methods & Applications, 2002, vol. 11, issue 1, No 4, 82 pages

Abstract: Abstract In principal component analysis (PCA), it is crucial to know how many principal components (PCs) should be retained in order to account for most of the data variability. A class of “objective” rules for finding this quantity is the class of cross-validation (CV) methods. In this work we compare three CV techniques showing how the performance of these methods depends on the covariance matrix structure. Finally we propose a rule for the choice of the “best” CV method and give an application to real data.

Keywords: Principal component analysis; cross-validation methods (search for similar items in EconPapers)
Date: 2002
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DOI: 10.1007/BF02511446

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