论文标题
来自压缩数据的有效数据感知协方差估计器
Effective Data-aware Covariance Estimator from Compressed Data
论文作者
论文摘要
从大量的高维数据和分布式数据中估算协方差矩阵对于各种现实世界应用很重要。在本文中，我们提出了基于数据感知加权采样的协方差矩阵估计器，即DACE，该估计器可以提供无偏的协方差矩阵估计，并在相同的压缩比下获得更准确的估计。此外，我们将提议的DACE扩展到解决多类别的分类问题中，并通过理论依据来解决合成和现实世界数据集的广泛实验，以证明我们的DACE的出色性能。
Estimating covariance matrix from massive high-dimensional and distributed data is significant for various real-world applications. In this paper, we propose a data-aware weighted sampling based covariance matrix estimator, namely DACE, which can provide an unbiased covariance matrix estimation and attain more accurate estimation under the same compression ratio. Moreover, we extend our proposed DACE to tackle multiclass classification problems with theoretical justification and conduct extensive experiments on both synthetic and real-world datasets to demonstrate the superior performance of our DACE.
