论文标题
测量条件分布之间的差异：方法，属性和应用
Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications
论文作者
论文摘要
我们提出了一个简单而强大的测试统计量，以量化两个条件分布之间的差异。新的统计量避免了对高度空间中基本分布的明确估计，并且它使用Bregman矩阵差异在对称阳性半芬矿（SPS）矩阵的锥上工作。此外，它继承了Correntropy函数的优点，可以在数据中明确地纳入高阶统计信息。我们介绍了新统计数据的属性，并说明了其与先前艺术的联系。我们最终显示了我们在三个不同的机器学习问题上的新统计数据的应用，即对图表的多任务学习，概念漂移检测和信息理论特征选择，以证明其效用和优势。我们的统计代码可在https://bit.ly/bregmancorrentropy上获得。
We propose a simple yet powerful test statistic to quantify the discrepancy between two conditional distributions. The new statistic avoids the explicit estimation of the underlying distributions in highdimensional space and it operates on the cone of symmetric positive semidefinite (SPS) matrix using the Bregman matrix divergence. Moreover, it inherits the merits of the correntropy function to explicitly incorporate high-order statistics in the data. We present the properties of our new statistic and illustrate its connections to prior art. We finally show the applications of our new statistic on three different machine learning problems, namely the multi-task learning over graphs, the concept drift detection, and the information-theoretic feature selection, to demonstrate its utility and advantage. Code of our statistic is available at https://bit.ly/BregmanCorrentropy.
