Too Many or Too Few? Sampling Bounds for Topological Descriptors

TL;DR

This paper investigates the optimal sampling bounds for topological descriptors like Euler characteristic and persistence diagrams, balancing between oversampling for accuracy and undersampling for efficiency, with theoretical and experimental insights.

Contribution

It provides new bounds and constructive proofs for the number of samples needed to accurately represent topological features of shapes.

Findings

Bounds on sampling density for topological descriptors

Trade-offs between sampling size and topological fidelity

Experimental validation on synthetic and real data

Abstract

Topological descriptors, such as the Euler characteristic function and the persistence diagram, have grown increasingly popular for representing complex data. Recent work showed that a carefully chosen set of these descriptors encodes all of the geometric and topological information about a shape in R^d. In practice, epsilon nets are often used to find samples in one of two extremes. On one hand, making strong geometric assumptions about the shape allows us to choose epsilon small enough (corresponding to a high enough density sample) in order to guarantee a faithful representation, resulting in oversampling. On the other hand, if we choose a larger epsilon in order to allow faster computations, this leads to an incomplete description of the shape and a discretized transform that lacks theoretical guarantees. In this work, we investigate how many directions are really needed to represent geometric simplicial complexes, exploring both synthetic and real-world datasets. We provide constructive proofs that help establish size bounds and an experimental investigation giving insights into the consequences of over- and undersampling.