Ortak Bilgi Miktarının Modelden-Baǧımsız ve Hızlı Hesaplanması için Yeni Yöntemler

Abstract

In this study, two new approaches are proposed to calculate mutual information between two random variables from data. These approaches are constructed in a way to use the properties of the differential entropy under linear transformations and to try to minimize conditional entropy in a model-free manner. In comparisons with a widely used mutual information estimator, the Kraskov method, the methods that we termed as unit vector parametrization and data fitting based estimators, offered an advantage in terms of computation speed.