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.

Cross-validation methods in principal components: a comparison / Giancarlo Diana, Chiara Tommasi. - In: STATISTICAL METHODS & APPLICATIONS. - ISSN 1618-2510. - 11:1(2002), pp. 71-82.

Cross-validation methods in principal components: a comparison

C. Tommasi
2002

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/21914
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