Barycenters for the Hellinger--Kantorovich distance over ℝd
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Abstract (arXiv)
We study the barycenter of the Hellinger--Kantorovich metric over non-negative measures on compact, convex subsets of ℝd. The article establishes existence, uniqueness (under suitable assumptions) and equivalence between a coupled-two-marginal and a multi-marginal formulation. We analyze the HK barycenter between Dirac measures in detail, and find that it differs substantially from the Wasserstein barycenter by exhibiting a local `clustering' behaviour, depending on the length scale of the input measures. In applications it makes sense to simultaneously consider all choices of this scale, leading to a 1-parameter family of barycenters. We demonstrate the usefulness of this family by analyzing point clouds sampled from a mixture of Gaussians and inferring the number and location of the underlying Gaussians.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Oct 2019.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 2.
4 papers.
- 2024Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transportcited by 9
- 2022Wasserstein Distributionally Robust Optimization with Wasserstein Barycenterscited by 3
- 2025Conic Formulations of Transport Metrics for Unbalanced Measure Networks and Hypernetworkscited by 2
- 2025Lower Bounds on Adversarial Robustness for Multiclass Classification with General Loss Functionscited by 1
- 2025Lower Bounds on Adversarial Robustness for Multiclass Classification with General Loss Functions2 Oct 2025
- 2025Conic Formulations of Transport Metrics for Unbalanced Measure Networks and Hypernetworks14 Aug 2025
- 2024Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport4 Oct 2024
- 2022Wasserstein Distributionally Robust Optimization with Wasserstein Barycenters23 Mar 2022