Kan Approximations of the Persistent Homology Transform

TL;DR

This paper introduces a method to approximate the persistent homology transform (PHT) of a shape using finite directional samples, enabling interpolation and close approximation of the true PHT with theoretical guarantees.

Contribution

It provides the first theoretical framework for approximating the PHT from finite directional and scalar data using Kan extensions.

Findings

Interpolation of PHT from finite samples is possible.

The approximation can be made arbitrarily close to the true PHT.

The method applies to both directional and scalar data samples.

Abstract

The persistent homology transform (PHT) of a subset $M \subset \mathbb{R}^d$ is a map $\text{PHT}(M):\mathbb{S}^{d-1} \to \mathbf{Dgm}$ from the unit sphere to the space of persistence diagrams. This map assigns to each direction $v\in \mathbb{S}^{d-1}$ the persistent homology of the filtration of $M$ in direction $v$. In practice, one can only sample the map $\text{PHT}(M)$ at a finite set of directions $A \subset \mathbb{S}^{d-1}$. This suggests two natural questions: (1) Can we interpolate the PHT from this finite sample of directions to the entire sphere? If so, (2) can we prove that the resulting interpolation is close to the true PHT? In this paper we show that if we can sample the PHT at the module level, where we have information about how homology from each direction interacts, a ready-made interpolation theory due to Bubenik, de Silva, and Nanda using Kan extensions can answer both of these questions in the affirmative. A close inspection of those techniques shows that we can infer the PHT from a finite sample of heights from each direction as well. Our paper presents the first known results for approximating the PHT from finite directional and scalar data.