Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications
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Publication:6340073
arXiv2005.02196MaRDI QIDQ6340073
Author name not available (Why is that?)
Publication date: 5 May 2020
Abstract: 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.
Has companion code repository: https://github.com/SJYuCNEL/Bregman-Correntropy-Conditional-Divergence
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