Kernel Measures of Conditional Dependence
Kernel Measures of Conditional Dependence
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发表时间：
2007-12
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通讯作者：
K. Fukumizu;A. Gretton;Xiaohai Sun;B. Scholkopf
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作者：
K. Fukumizu;A. Gretton;Xiaohai Sun;B. Scholkopf
我们提出了一种基于再生核希尔伯特空间上的归一化互协方差算子的随机变量条件依赖性的新度量。与以前的核依赖性测量不同，对于多种核，所提出的标准不依赖于无限数据限制下核的选择。同时，它具有简单的经验估计和良好的收敛行为。我们讨论了该方法的理论特性，并展示了其在实验中的应用。
We propose a new measure of conditional dependence of random variables, based on normalized cross-covariance operators on reproducing kernel Hilbert spaces. Unlike previous kernel dependence measures, the proposed criterion does not depend on the choice of kernel in the limit of infinite data, for a wide class of kernels. At the same time, it has a straightforward empirical estimate with good convergence behaviour. We discuss the theoretical properties of the measure, and demonstrate its application in experiments.