mlsanityData to 14 Oct 2026

Geometric Jensen-Shannon Divergence Between Gaussian Measures On Hilbert Space

arXiv 2506.10494 ↗First posted 12 Jun 2025math.PR cs.IT math.ITcited by 1

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Abstract (arXiv)

This work studies the Geometric Jensen-Shannon divergence, based on the notion of geometric mean of probability measures, in the setting of Gaussian measures on an infinite-dimensional Hilbert space. On the set of all Gaussian measures equivalent to a fixed one, we present a closed form expression for this divergence that directly generalizes the finite-dimensional version. Using the notion of Log-Determinant divergences between positive definite unitized trace class operators, we then define a Regularized Geometric Jensen-Shannon divergence that is valid for any pair of Gaussian measures and that recovers the exact Geometric Jensen-Shannon divergence between two equivalent Gaussian measures when the regularization parameter tends to zero.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 29 references that are in the map. First posted on arXiv 12 Jun 2025.

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4 of 29 references.

References · 4

  1. 2019On a generalization of the Jensen-Shannon divergence and the JS-symmetrization of distances relying on abstract meanscited by 61
  2. 2022Kullback-Leibler and Renyi divergences in reproducing kernel Hilbert space and Gaussian process settingscited by 4
  3. 2013Bootstrap Methods for the Empirical Study of Decision-Making and Information Flows in Social Systemscited by 3
  4. 2021Estimation of Riemannian distances between covariance operators and Gaussian processescited by 3

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