Approximation Algorithms for 1-Wasserstein Distance Between Persistence Diagrams

Authors Samantha Chen, Yusu Wang

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Author Details

Samantha Chen
  • University of California at San Diego, La Jolla, CA, USA
Yusu Wang
  • University of California at San Diego, La Jolla, CA, USA


We want to thank Chen Cai for providing the reddit-binary and ModelNet10 datasets used in the experiments.

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Samantha Chen and Yusu Wang. Approximation Algorithms for 1-Wasserstein Distance Between Persistence Diagrams. In 19th International Symposium on Experimental Algorithms (SEA 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 190, pp. 14:1-14:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


Recent years have witnessed a tremendous growth using topological summaries, especially the persistence diagrams (encoding the so-called persistent homology) for analyzing complex shapes. Intuitively, persistent homology maps a potentially complex input object (be it a graph, an image, or a point set and so on) to a unified type of feature summary, called the persistence diagrams. One can then carry out downstream data analysis tasks using such persistence diagram representations. A key problem is to compute the distance between two persistence diagrams efficiently. In particular, a persistence diagram is essentially a multiset of points in the plane, and one popular distance is the so-called 1-Wasserstein distance between persistence diagrams. In this paper, we present two algorithms to approximate the 1-Wasserstein distance for persistence diagrams in near-linear time. These algorithms primarily follow the same ideas as two existing algorithms to approximate optimal transport between two finite point-sets in Euclidean spaces via randomly shifted quadtrees. We show how these algorithms can be effectively adapted for the case of persistence diagrams. Our algorithms are much more efficient than previous exact and approximate algorithms, both in theory and in practice, and we demonstrate its efficiency via extensive experiments. They are conceptually simple and easy to implement, and the code is publicly available in github.

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
  • persistence diagrams
  • approximation algorithms
  • Wasserstein distance
  • optimal transport


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