TL;DR
This paper introduces a new family of fractal dimensions for probability measures using persistent homology, linking topological data analysis with fractal geometry, and explores their properties and potential applications.
Contribution
It defines a novel fractal dimension based on persistent homology intervals and proves the existence of a limiting distribution curve for certain measures, extending prior work on minimal spanning trees.
Findings
For measures supported on Euclidean spaces, the 0-dimensional dimension equals the ambient space dimension if the measure has a positive absolutely continuous part.
A limiting distribution curve for persistent homology intervals exists for the uniform measure on the unit interval.
Conjecture: similar limiting curves exist for measures on compact sets with positive Lebesgue measure.
Abstract
We use persistent homology in order to define a family of fractal dimensions, denoted for each homological dimension , assigned to a probability measure on a metric space. The case of -dimensional homology () relates to work by Michael J Steele (1988) studying the total length of a minimal spanning tree on a random sampling of points. Indeed, if is supported on a compact subset of Euclidean space for , then Steele's work implies that if the absolutely continuous part of has positive mass, and otherwise . Experiments suggest that similar results may be true for higher-dimensional homology , though this is an open question. Our fractal dimension is defined by considering a limit, as the number of points goes to…
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