# Topological Inference via Meshing

3 GEOMETRICA - Geometric computing
CRISAM - Inria Sophia Antipolis - Méditerranée
4 GEOMETRICA - Geometric computing
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
Abstract : We apply ideas from mesh generation to improve the time and space complexities of computing the full persistent homological information associated with a point cloud $P$ in Euclidean space $\R^d$. Classical approaches rely on the \v Cech, Rips, $\alpha$-complex, or witness complex filtrations of $P$, whose complexities scale up very badly with $d$. For instance, the $\alpha$-complex filtration incurs the $n^{\Omega(d)}$ size of the Delaunay triangulation, where $n$ is the size of $P$. The common alternative is to truncate the filtrations when the sizes of the complexes become prohibitive, possibly before discovering the most relevant topological features. In this paper we propose a new collection of filtrations, based on the Delaunay triangulation of a carefully-chosen superset of $P$, whose sizes are reduced to $2^{O(d^2)}n$. A nice property of these filtrations is to be interleaved multiplicatively with the family of offsets of $P$, so that the persistence diagram of $P$ can be approximated in $2^{O(d^2)}n^3$ time in theory, with a near-linear observed running time in practice (ignoring the constant factors depending exponentially on $d$). Thus, our approach remains tractable in medium dimensions, say 4 to 10.
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https://hal.inria.fr/inria-00436891
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Submitted on : Tuesday, December 1, 2009 - 7:36:44 PM
Last modification on : Thursday, August 22, 2019 - 12:10:38 PM
Long-term archiving on: : Saturday, November 26, 2016 - 2:44:13 PM

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RR-7125.pdf
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• HAL Id : inria-00436891, version 2

### Citation

Benoît Hudson, Gary Miller, Steve Oudot, Donald Sheehy. Topological Inference via Meshing. [Research Report] RR-7125, 2009. ⟨inria-00436891v2⟩

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