论文标题
基于几何中位数和中值协方差矩阵的强大基于模型的聚类
A robust model-based clustering based on the geometric median and the Median Covariation Matrix
论文作者
论文摘要
将观察结果分组为均质组是统计数据分析中的一项复发任务。我们认为高斯混合模型是最著名的基于参数模型的聚类方法。我们为基于模型的聚类提出了一种新的可靠方法，该方法包括对EM算法的修改（更具体地说，是M-step），通过取代基于中位数和中位数协方差矩阵的均值版本的估计值和稳健版本的差异。所有提出的方法均在R cran上可访问的r toppains rgmm中可用。
Grouping observations into homogeneous groups is a recurrent task in statistical data analysis. We consider Gaussian Mixture Models, which are the most famous parametric model-based clustering method. We propose a new robust approach for model-based clustering, which consists in a modification of the EM algorithm (more specifically, the M-step) by replacing the estimates of the mean and the variance by robust versions based on the median and the median covariation matrix. All the proposed methods are available in the R package RGMM accessible on CRAN.
