摘要翻译：
提出了一种基于广义基尼相关指数的主成分分析方法（基尼PCA）。基尼主成分分析是基于方差的标准主成分分析的推广。结果表明，在高斯情形下，标准主成分分析与基尼主成分分析是等价的。本文还证明了基于广义基尼相关矩阵的降维方法对离群点具有较强的鲁棒性。Monte Carlo仿真和在cars数据（含离群点）上的应用表明了Gini PCA的鲁棒性，并与方差PCA相比对结果提供了不同的解释。
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英文标题：
《Principal Component Analysis: A Generalized Gini Approach》
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作者：
Charpentier, Arthur and Mussard, Stephane and Tea Ouraga
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最新提交年份：
2019
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/1910.10133