Principal Component Analysis: A Generalized Gini Approach

Our paper, with Stéphane Mussard and Téa Ouraga, entitle Principal Component Analysis: A Generalized Gini Approach is finally out in the European Journal of Operations Research.

A principal component analysis based on the generalized Gini correlation index is proposed (Gini PCA). The Gini PCA generalizes the standard PCA based on the variance. It is shown, in the Gaussian case, that the standard PCA is equivalent to the Gini PCA. It is also proven that the dimensionality reduction based on the generalized Gini correlation matrix, that relies on city-block distances, is robust to outliers. Monte Carlo simulations and an application on cars data (with outliers) show the robustness of the Gini PCA and provide different interpretations of the results compared with the variance PCA.


OpenEdition suggests that you cite this post as follows:
Arthur Charpentier (February 22, 2021). Principal Component Analysis: A Generalized Gini Approach. Freakonometrics. Retrieved March 18, 2025 from https://doi.org/10.58079/ovhr


Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.