Results 61 to 70 of about 195 (142)

Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance. [PDF]

open access: yesNeuroimage, 2023
Chung MK   +9 more
europepmc   +1 more source

Lower Bounding the Gromov--Hausdorff distance in Metric Graphs

open access: yes
Let $G$ be a finite, connected metric graph and let $X\subseteq G$ be a subset. If $X$ is sufficiently dense in $G$, we show that the Gromov--Hausdorff distance matches the Hausdorff distance, namely $d_\gh(G,X)=d_\h(G,X)$. When the metric graph is the circle $G=S^1$ with circumference $2π$, a recent study established the equality $d_\gh(S^1,X)=d_\h(S ...
Adams, Henry   +4 more
openaire   +3 more sources

GROMOV--HAUSDORFF DISTANCES BETWEEN NORMED SPACES

open access: yesMatematički Vesnik
In the present paper we study the original Gromov-Hausdorff distance between real normed spaces. In the first part of the paper we prove that two finite-dimensional real normed spaces on a finite Gromov-Hausdorff distance are isometric to each other.
openaire   +3 more sources

Lectures on Hausdorff and Gromov-Hausdorff Distance Geometry

open access: yes, 2020
The course was given at Peking University, Fall 2019. We discuss the following subjects: (1) Introduction to general topology, hyperspaces, metric and pseudometric spaces, graph theory. (2) Graphs in metric spaces, minimum spanning tree, Steiner minimal tree, Gromov minimal filling.
openaire   +2 more sources

Fundamentals of Theory of Continuous Gromov--Hausdorff distance

open access: yes
The Gromov--Hausdorff distance (hereinafter referred to as the GH-distance) is a measure of non-isometricity of metric spaces. In this paper, we study a modification of this distance that also takes topological differences into account. The resulting function of pairs of metric spaces is called the continuous GH-distance.
Bogaty, Semeon A., Tuzhilin, Alexey A.
openaire   +2 more sources

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