# Principal Component Analysis: A Generalized Gini Approach

arXiv: https://arxiv.org/abs/1910.10133 · HTML: https://mlsanity.com/paper/1910.10133/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [KNN and K-means in Gini Prametric Spaces](https://mlsanity.com/paper/2501.18028/) (2025)
- [Structural Concentration in Weighted Networks: A Class of Topology-Aware Indices](https://mlsanity.com/paper/2603.21918/) (2026)

## Abstract (arXiv)

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.