Geometric Estimation of Multivariate Dependency
Abstract
This paper proposes a geometric estimator of dependency between a pair of multivariate samples. The proposed estimator of dependency is based on a randomly permuted geometric graph (the minimal spanning tree) over the two multivariate samples. This estimator converges to a quantity that we call the geometric mutual information (GMI), which is equivalent to the Henze-Penrose divergence [1] between the joint distribution of the multivariate...
Paper Details
Title
Geometric Estimation of Multivariate Dependency
Published Date
May 21, 2019
Journal
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