Detection of Small Holes by the Scale-Invariant Robust Density-Aware Distance (RDAD) Filtration
arXiv:2204.07821 · doi:10.1007/s41468-024-00166-9
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.
39 pages, 38 figs, J Appl. and Comput. Topology (2024). GitHub: [github.com/c-siu/RDAD]. Published version: [rdcu.be/dCXLa]. Diff of v2/3: added publication info, NO post-submission improvements (Cor2-3 rephrased and proven, setup of Sec4.1 explained, complexity computed in Sec6.1, Thm5 simplified, comparison with DTM in Sec1,8, streamlining), so no change in pdf. Diff of v1/2: more thms, more discussion on conformality, fewer egs