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Pomerantsev AL, Rodionova OY. Procrustes Cross-Validation of short datasets in PCA context. Talanta. 2021;226:122104doi: 10.1016/j.talanta.2021.122104.
Pomerantsev, A. L., & Rodionova, O. Y. (2021). Procrustes Cross-Validation of short datasets in PCA context. Talanta, 226122104. https://doi.org/10.1016/j.talanta.2021.122104
Pomerantsev, Alexey L, and Rodionova, Oxana Ye. "Procrustes Cross-Validation of short datasets in PCA context." Talanta vol. 226 (2021): 122104. doi: https://doi.org/10.1016/j.talanta.2021.122104
Pomerantsev AL, Rodionova OY. Procrustes Cross-Validation of short datasets in PCA context. Talanta. 2021 May 01;226:122104. doi: 10.1016/j.talanta.2021.122104. Epub 2021 Jan 15. PMID: 33676660.
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Talanta. 2021 May 01;226:122104. doi: 10.1016/j.talanta.2021.122104. Epub 2021 Jan 15.

Procrustes Cross-Validation of short datasets in PCA context.

Talanta

Alexey L Pomerantsev, Oxana Ye Rodionova

Affiliations

  1. Semenov Federal Research Center for Chemical Physics RAS, Kosygin Str. 4, 119991, Moscow, Russia. Electronic address: [email protected].
  2. Semenov Federal Research Center for Chemical Physics RAS, Kosygin Str. 4, 119991, Moscow, Russia.

PMID: 33676660 DOI: 10.1016/j.talanta.2021.122104

Abstract

We suggest using a new tool, Procrustes cross-validation, as an alternative to a regular cross-validation for short datasets where each sample is important and, therefore, cannot be removed in line with the conventional leave-one-out cross-validation procedure. The advantages of the new approach are demonstrated using two real-world examples: the first one contains discrete variables (chemical profiles). The second one is based on continuous data (spectra). The method is implemented in R and Matlab as a small procedure that any analyst can easily use.

Copyright © 2021 Elsevier B.V. All rights reserved.

Keywords: Designed data; Procrustes cross-validation; Small data

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