Relying on recent advances in statistical estimation of covariance distances based on random matrix theory, this article proposes an improved covariance and precision matrix estimation for a wide family of metrics. The method is shown to largely outperform the sample covariance matrix estimate and to compete with state-of-the-art methods, while at the same time being computationally simpler. Applications to linear and quadratic discriminant analyses also demonstrate significant gains, therefore suggesting practical interest to statistical machine learning.

中文翻译：

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随机矩阵改进了大量指标的协方差估计*

依靠基于随机矩阵理论的协​​方差距离统计估计的最新进展，本文提出了一种改进的协方差和精度矩阵估计，用于广泛的度量系列。该方法被证明在很大程度上优于样本协方差矩阵估计并与最先进的方法竞争，同时在计算上更简单。线性和二次判别分析的应用也显示出显着的收益，因此表明对统计机器学习的实际兴趣。