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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Diana, G., Tommasi, C. Cross-validation methods in principal component analysis: A comparison. Statistical Methods & Applications 11, 71–82 (2002). https://doi.org/10.1007/BF02511446
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DOI: https://doi.org/10.1007/BF02511446