Metricizing the Euclidean Space towards Desired Distance Relations in Point Clouds
Rass, Stefan, König, Sandra, Ahmad, Shahzad, Goman, Maksim
–arXiv.org Artificial Intelligence
Given a set of points in the Euclidean space $\mathbb{R}^\ell$ with $\ell>1$, the pairwise distances between the points are determined by their spatial location and the metric $d$ that we endow $\mathbb{R}^\ell$ with. Hence, the distance $d(\mathbf x,\mathbf y)=\delta$ between two points is fixed by the choice of $\mathbf x$ and $\mathbf y$ and $d$. We study the related problem of fixing the value $\delta$, and the points $\mathbf x,\mathbf y$, and ask if there is a topological metric $d$ that computes the desired distance $\delta$. We demonstrate this problem to be solvable by constructing a metric to simultaneously give desired pairwise distances between up to $O(\sqrt\ell)$ many points in $\mathbb{R}^\ell$. We then introduce the notion of an $\varepsilon$-semimetric $\tilde{d}$ to formulate our main result: for all $\varepsilon>0$, for all $m\geq 1$, for any choice of $m$ points $\mathbf y_1,\ldots,\mathbf y_m\in\mathbb{R}^\ell$, and all chosen sets of values $\{\delta_{ij}\geq 0: 1\leq i
arXiv.org Artificial Intelligence
Apr-25-2023
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