Gromov-Hausdorff limit of Wasserstein spaces on point clouds
Abstract
We consider a point cloud $X_n := \{ x_1, \dots, x_n \}$ uniformly distributed on the flat torus $\mathbb{T}^d : = \mathbb{R}^d / \mathbb{Z}^d $, and construct a geometric graph on the cloud by connecting points that are within distance $\varepsilon$ of each other. We let $\mathcal{P}(X_n)$ be the space of probability measures on $X_n$ and endow it with a discrete Wasserstein distance $W_n$ as introduced independently by Chow et al, Maas, and Mielke for general finite Markov chains. We show that as long as $\varepsilon= \varepsilon_n$ decays towards zero slower than an explicit rate depending on the level of uniformity of $X_n$, then the space $(\mathcal{P}(X_n), W_n)$ converges in the Gromov-Hausdorff sense towards the space of probability measures on $\mathbb{T}^d$ endowed with the Wasserstein distance. The analysis presented in this paper is a first step in the study of stability of evolution equations defined over random point clouds as the number of points grows to infinity.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2017
- DOI:
- 10.48550/arXiv.1702.03464
- arXiv:
- arXiv:1702.03464
- Bibcode:
- 2017arXiv170203464G
- Keywords:
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- Mathematics - Metric Geometry;
- Mathematics - Analysis of PDEs;
- Mathematics - Probability;
- Mathematics - Statistics Theory;
- Statistics - Machine Learning;
- 49J45 49J55 49J15 35K05