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Kernel density estimation on spaces of Gaussian distributions and symmetric positive definite matrices

Abstract : This paper analyses the kernel density estimation on spaces of Gaussian distributions endowed with different metrics. Explicit expressions of kernels are provided for the case of the 2-Wasserstein metric on multivariate Gaussian distributions and for the Fisher metric on multivariate centred distributions. Under the Fisher metric, the space of multivariate centred Gaussian distributions is isometric to the space of symmetric positive definite matrices under the affine-invariant metric and the space of univariate Gaussian distributions is isometric to the hyperbolic space. Thus kernel are also valid on these spaces. The density estimation is successfully applied to a classification problem of electro-encephalographic signals.
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https://hal.archives-ouvertes.fr/hal-01245712
Contributor : Emmanuel Chevallier Connect in order to contact the contributor
Submitted on : Thursday, December 17, 2015 - 3:21:12 PM
Last modification on : Wednesday, November 17, 2021 - 12:27:13 PM
Long-term archiving on: : Friday, March 18, 2016 - 2:00:40 PM

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  • HAL Id : hal-01245712, version 1

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Emmanuel Chevallier, Emmanuel Kalunga, Jesus Angulo. Kernel density estimation on spaces of Gaussian distributions and symmetric positive definite matrices. 2015. ⟨hal-01245712⟩

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