Principal component analysis: A generalized Gini approach
提出基于广义基尼相关系数的主成分分析（Gini PCA），它比传统方差PCA更抗异常值干扰，在高斯情形下两者等价，蒙特卡洛模拟和汽车数据应用验证了其稳健性。
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.