Fractal Dimension Estimation with Persistent Homology: A Comparative Study

Abstract

We propose that the recently defined persistent homology dimensions are a practical tool for fractal dimension estimation of point samples. We implement an algorithm to estimate the persistent homology dimension, and compare its performance to classical methods to compute the correlation and box-counting dimensions in examples of self-similar fractals, chaotic attractors, and an empirical dataset. The performance of the $0$-dimensional persistent homology dimension is comparable to that of the correlation dimension, and better than box-counting.

24 pages, 13 figures

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• Applications of Persistent Homology in Nuclear Collisions
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• Alpha magnitude
• The self-similar evolution of stationary point processes via persistent homology
• Detection of Small Holes by the Scale-Invariant Robust Density-Aware Distance (RDAD) Filtration