Kernel Density Estimation on Spaces of Gaussian Distributions and Symmetric Positive Definite Matrices
Abstract
This paper analyzes the kernel density estimation on spaces of Gaussian distributions endowed with different metrics. Expressions of kernels are provided for the 2-Wasserstein metric on the space of multivariate Gaussians. For the Fisher metric the kernels are provided only for univariate Gaussians and multivariate centered Gaussians. The density estimation is successfully applied to a classification problem of electro-encephalographic signals.
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© 2017, Society for Industrial and Applied Mathematics.
History
Submitted: 21 December 2015
Accepted: 7 November 2016
Published online: 8 February 2017
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