The properties of penalized sample covariance matrices depend on the choice of the penalty function. In this paper, we introduce a class of non-smooth penalty functions for the sample covariance matrix, and demonstrate how this method results in a grouping of the estimated eigenvalues. We refer to this method as "lassoing eigenvalues" or as the "elasso".

中文翻译：

---

套索特征值

惩罚样本协方差矩阵的属性取决于惩罚函数的选择。在本文中，我们为样本协方差矩阵引入了一类非平滑惩罚函数，并演示了该方法如何对估计的特征值进行分组。我们将此方法称为“套索特征值”或“elasso”。