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Journal Article

Kantorovich-Rubinstein Distance and Barycenter for Finitely Supported Measures: Foundations and Algorithms


Munk,  Axel
Research Group of Statistical Inverse Problems in Biophysics, Max Planck Institute for Multidisciplinary Sciences, Max Planck Society;

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Heinemann, F., Klatt, M., & Munk, A. (2022). Kantorovich-Rubinstein Distance and Barycenter for Finitely Supported Measures: Foundations and Algorithms. Applied Mathematics & Optimization, 87(1): 4. doi:10.1007/s00245-022-09911-x.

Cite as: https://hdl.handle.net/21.11116/0000-000C-01F6-D
The purpose of this paper is to provide a systematic discussion of a generalized barycenter based on a variant of unbalanced optimal transport (UOT) that defines a distance between general non-negative, finitely supported measures by allowing for mass creation and destruction modeled by some cost parameter. They are denoted as Kantorovich–Rubinstein (KR) barycenter and distance. In particular, we detail the influence of the cost parameter to structural properties of the KR barycenter and the KR distance. For the latter we highlight a closed form solution on ultra-metric trees. The support of such KR barycenters of finitely supported measures turns out to be finite in general and its structure to be explicitly specified by the support of the input measures. Additionally, we prove the existence of sparse KR barycenters and discuss potential computational approaches. The performance of the KR barycenter is compared to the OT barycenter on a multitude of synthetic datasets. We also consider barycenters based on the recently introduced Gaussian Hellinger–Kantorovich and Wasserstein–Fisher–Rao distances.