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Procrustes cross-validation of multivariate regression models.

Sergey Kucheryavskiy1, Oxana Rodionova2, Alexey Pomerantsev2

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A new, faster method called Generalized Procrustes Cross-Validation enhances chemometric model validation. This approach offers advanced tools for dataset analysis, improving the reliability of chemical data interpretation.

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Area of Science:

  • Chemometrics
  • Analytical Chemistry
  • Data Science

Background:

  • Procrustes Cross-Validation (PCV) is a method for validating chemometric models.
  • Existing methods may have limitations in speed and scope.
  • Effective model validation is crucial for reliable data analysis in chemistry.

Purpose of the Study:

  • To propose a generalization of Procrustes Cross-Validation (PCV).
  • To enhance the speed and applicability of PCV for chemometric model validation.
  • To introduce new analytical tools for dataset exploration within the validation framework.

Main Methods:

  • Methodological description of the generalized Procrustes Cross-Validation approach.
  • Application of Procrustean rules and mathematical principles.
  • Validation using real-world chemical datasets.

Main Results:

  • The generalized approach is significantly faster than its predecessor.
  • The method is applicable to a broader range of chemometric models.
  • New functionalities for dataset heterogeneity, cross-validation split quality, and outlier detection are provided.

Conclusions:

  • The generalized Procrustes Cross-Validation offers a more efficient and versatile tool for chemometric model validation.
  • The enhanced approach provides deeper insights into dataset characteristics.
  • Practical application on chemical datasets demonstrates the method's utility and effectiveness.