Parallelly Sliced Optimal Transport on Spheres and on the Rotation Group

Author:

Quellmalz Michael,Buecher Léo,Steidl Gabriele

Abstract

AbstractSliced optimal transport, which is basically a Radon transform followed by one-dimensional optimal transport, became popular in various applications due to its efficient computation. In this paper, we deal with sliced optimal transport on the sphere $$\mathbb {S}^{d-1}$$ S d - 1 and on the rotation group $$\textrm{SO}(3)$$ SO ( 3 ) . We propose a parallel slicing procedure of the sphere which requires again only optimal transforms on the line. We analyze the properties of the corresponding parallelly sliced optimal transport, which provides in particular a rotationally invariant metric on the spherical probability measures. For $$\textrm{SO}(3)$$ SO ( 3 ) , we introduce a new two-dimensional Radon transform and develop its singular value decomposition. Based on this, we propose a sliced optimal transport on $$\textrm{SO}(3)$$ SO ( 3 ) . As Wasserstein distances were extensively used in barycenter computations, we derive algorithms to compute the barycenters with respect to our new sliced Wasserstein distances and provide synthetic numerical examples on the 2-sphere that demonstrate their behavior for both the free- and fixed-support setting of discrete spherical measures. In terms of computational speed, they outperform the existing methods for semicircular slicing as well as the regularized Wasserstein barycenters.

Funder

Technische Universität Berlin

Publisher

Springer Science and Business Media LLC

Reference79 articles.

1. Abramowitz, M., Stegun, I.A. (eds.): Handbook of Mathematical Functions. National Bureau of Standards, Washington, DC (1972)

2. Absil, P.-A., Mahony, R., Sepulchre, R.: Optimization Algorithms on Matrix Manifolds. Princeton University Press, Princeton (2008). https://doi.org/10.1515/9781400830244

3. Agueh, M., Carlier, G.: Barycenters in the Wasserstein space. SIAM J. Math. Anal. 43(2), 904–924 (2011). https://doi.org/10.1137/100805741

4. Altekrüger, F., Hertrich, J., Steidl, G.: Neural Wasserstein gradient flows for maximum mean discrepancies with Riesz kernels. In: Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., Scarlett, J. (eds.) Proceedings of the 40th International Conference on Machine Learning, pp. 664–690. PMLR (2023). https://proceedings.mlr.press/v202/altekruger23a.html

5. Ambrosio, L., Gigli, N., Savaré, G.: Gradient flows in metric spaces and in the space of probability measures. Lectures in Mathematics ETH Zürich. Birkhäuser, Basel (2005). https://doi.org/10.1007/b137080

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