Dimensionality Reduction for $k$-Distance Applied to Persistent Homology

Shreya Arya; Jean-Daniel Boissonnat; Kunal Dutta; Martin Lotz

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arXiv · 2110.05897

Dimensionality Reduction for $k$-Distance Applied to Persistent Homology

Shreya Arya·Jean-Daniel Boissonnat·Kunal Dutta·Martin Lotz

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Abstract

Given a set P of n points and a constant k, we are interested in computing the persistent homology of the Cech filtration of P for the k-distance, and investigate the effectiveness of dimensionality reduction for this problem, answering an open question of Sheehy [Proc. SoCG, 2014]. We show that any linear transformation that preserves pairwise distances up to a (1 +/- e) multiplicative factor, must preserve the persistent homology of the Cech filtration up to a factor of (1-e)^(-1). Our results also show that the Vietoris-Rips and Delaunay filtrations for the k-distance, as well as the Cech filtration for the approximate k-distance of Buchet et al. [J. Comput. Geom., 2016] are preserved up to a (1 +/- e) factor. We also prove extensions of our main theorem, for point sets (i) lying in a region of bounded Gaussian width or (ii) on a low-dimensional submanifold, obtaining embeddings having the dimension bounds of Lotz [Proc. Roy. Soc., 2019] and Clarkson [Proc. SoCG, 2008] respectively. Our results also work in the terminal dimensionality reduction setting, where the distance of any point in the original ambient space, to any point in P, needs to be approximately preserved.

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Shreya Arya, Jean-Daniel Boissonnat, Kunal Dutta, Martin Lotz. 2021-10-12. Dimensionality Reduction for $k$-Distance Applied to Persistent Homology. https://arxiv.org/abs/2110.05897

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