相关矩阵的收缩和谱滤波：通过Kullback-Leibler距离的比较
Shrinkage and spectral filtering of correlation matrices: a comparison via the Kullback-Leibler distance
摘要：
从大型相关矩阵中滤除信息的问题在许多应用中具有重要意义。我们最近提出了使用的Kullback-Leibler距离来衡量过滤算法的性能，在恢复相关矩阵的变量时，描述了一个多元高斯分布。在这里，我们使用Kullback-Leibler距离研究基于随机矩阵理论和收缩技术的滤波方法的性能。我们还提出了一些结果的应用程序的Kullback-Leibler距离的多元数据是非高斯分布。
英文摘要：
The problem of filtering information from large correlation matrices is of great importance in many applications. We have recently proposed the use of the Kullback-Leibler distance to measure the performance of filtering algorithms in recovering the underlying correlation matrix when the variables are described by a multivariate Gaussian distribution. Here we use the Kullback-Leibler distance to investigate the performance of filtering methods based on Random Matrix Theory and on the shrinkage technique. We also present some results on the application of the Kullback-Leibler distance to multivariate data which are non Gaussian distributed.
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