Tight bounds for the learning of homotopy \`a la Niyogi, Smale, and Weinberger for subsets of Euclidean spaces and of Riemannian manifolds
Dominique Attali, Hana Dal Poz Kou\v{r}imsk\'a, Christopher, Fillmore, Ishika Ghosh, Andr\'e Lieutier, Elizabeth Stephenson and, Mathijs Wintraecken

TL;DR
This paper extends the theoretical understanding of homotopy learning from samples of manifolds and sets of positive reach, providing explicit bounds and considering both Euclidean and Riemannian settings with bounded curvature.
Contribution
It generalizes previous work by considering sets of positive reach and Riemannian manifolds, introducing new bounds and a Riemannian reach concept.
Findings
Provided explicit bounds on sample proximity ensuring homotopy equivalence.
Extended results to Riemannian manifolds with bounded sectional curvature.
Introduced a new Riemannian reach concept and demonstrated tightness of bounds.
Abstract
In this article we extend and strengthen the seminal work by Niyogi, Smale, and Weinberger on the learning of the homotopy type from a sample of an underlying space. In their work, Niyogi, Smale, and Weinberger studied samples of manifolds with positive reach embedded in . We extend their results in the following ways: In the first part of our paper we consider both manifolds of positive reach -- a more general setting than manifolds -- and sets of positive reach embedded in . The sample of such a set does not have to lie directly on it. Instead, we assume that the two one-sided Hausdorff distances -- and -- between and are bounded. We provide explicit bounds in terms of and , that guarantee that there exists a parameter such that the union of balls of radius …
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