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Preprint · arXiv · December 12, 2019

Efficient Approximation of the Matching Distance for 2-Parameter Persistence

Michael Kerber, Arnur Nigmetov

Abstract

In topological data analysis, the matching distance is a computationally tractable metric on multi-filtered simplicial complexes. We design efficient algorithms for approximating the matching distance of two bi-filtered complexes to any desired precision ε>0. Our approach is based on a quad-tree refinement strategy introduced by Biasotti et al., but we recast their approach entirely in geometric terms. This point of view leads to several novel observations resulting in a practically faster algorithm. We demonstrate this speed-up by experimental comparison and provide our code in a public repository which provides the first efficient publicly available implementation of the matching distance.

Topological and Geometric Data AnalysisDigital Image Processing Techniques3D Shape Modeling and Analysismatching distanceapproximation algorithmstopological data analysis

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