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Cross-validation methods in principal component analysis: A comparison

Author

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  • Giancarlo Diana

    (Università di Padova)

  • Chiara Tommasi

    (Università di Padova)

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.

Suggested Citation

  • Giancarlo Diana & Chiara Tommasi, 2002. "Cross-validation methods in principal component analysis: A comparison," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(1), pages 71-82, February.
    • Handle: RePEc:spr:stmapp:v:11:y:2002:i:1:d:10.1007_bf02511446
    DOI: 10.1007/BF02511446
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    References listed on IDEAS

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    1. W. J. Krzanowski, 1987. "Selection of Variables to Preserve Multivariate Data Structure, Using Principal Components," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(1), pages 22-33, March.
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