摘要翻译：
从大的相关矩阵中过滤信息的问题在许多应用中都是非常重要的。我们最近提出了使用Kullback-Leibler距离来衡量滤波算法在用多元高斯分布描述变量时恢复底层相关矩阵的性能。本文利用Kullback-Leibler距离研究了基于随机矩阵理论和收缩技术的滤波方法的性能。我们也给出了Kullback-Leibler距离应用于非高斯分布的多元数据的一些结果。
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英文标题：
《Shrinkage and spectral filtering of correlation matrices: a comparison
  via the Kullback-Leibler distance》
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作者：
M. Tumminello, F. Lillo, R. N. Mantegna
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最新提交年份：
2007
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分类信息：

一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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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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PDF链接：
https://arxiv.org/pdf/0710.0576