Detection of Small Holes by the Scale-Invariant Robust Density-Aware Distance (RDAD) Filtration

TL;DR

This paper introduces the RDAD filtration, a topological data analysis method that effectively distinguishes small, high-density holes from noise by prolonging their persistence and enhancing robustness.

Contribution

The paper proposes the RDAD filtration, a novel TDA technique that improves detection of small features amidst noise by incorporating density weighting and distance-to-measure concepts.

Findings

RDAD filtration prolongs the persistence of small holes.

The method is robust against noise and outliers.

Numerical experiments demonstrate improved hole detection.

Abstract

A novel topological-data-analytical (TDA) method is proposed to distinguish, from noise, small holes surrounded by high-density regions of a probability density function. The proposed method is robust against additive noise and outliers. Traditional TDA tools, like those based on the distance filtration, often struggle to distinguish small features from noise, because both have short persistences. An alternative filtration, called the Robust Density-Aware Distance (RDAD) filtration, is proposed to prolong the persistences of small holes of high-density regions. This is achieved by weighting the distance function by the density in the sense of Bell et al. The concept of distance-to-measure is incorporated to enhance stability and mitigate noise. The persistence-prolonging property and robustness of the proposed filtration are rigorously established, and numerical experiments are presented to demonstrate the proposed filtration's utility in identifying small holes.