Improved Approximate Rips Filtrations with Shifted Integer Lattices and Cubical Complexes

TL;DR

This paper introduces a new scheme for approximating Rips filtrations efficiently using shifted integer lattices and cubical complexes, significantly reducing complexity while maintaining approximation guarantees.

Contribution

It presents a novel approximation scheme for Rips filtrations using integer lattices and cubical complexes, with improved size bounds and new techniques like acyclic carriers and scale balancing.

Findings

Achieves a 2-approximation in the L_infinity norm for Rips filtrations.

Reduces the size of the approximation to n2^{O(d)} cells with cubical complexes.

Extends the approximation to Euclidean spaces with a factor of 2d^{0.25}.

Abstract

Rips complexes are important structures for analyzing topological features of metric spaces. Unfortunately, generating these complexes is expensive because of a combinatorial explosion in the complex size. For $n$ points in $\mathbb{R}^d$, we present a scheme to construct a $2$-approximation of the filtration of the Rips complex in the $L_\infty$-norm, which extends to a $2d^{0.25}$-approximation in the Euclidean case. The $k$-skeleton of the resulting approximation has a total size of $n2^{O(d\log k +d)}$. The scheme is based on the integer lattice and simplicial complexes based on the barycentric subdivision of the $d$-cube. We extend our result to use cubical complexes in place of simplicial complexes by introducing cubical maps between complexes. We get the same approximation guarantee as the simplicial case, while reducing the total size of the approximation to only $n2^{O(d)}$ (cubical) cells. There are two novel techniques that we use in this paper. The first is the use of acyclic carriers for proving our approximation result. In our application, these are maps which relate the Rips complex and the approximation in a relatively simple manner and greatly reduce the complexity of showing the approximation guarantee. The second technique is what we refer to as scale balancing, which is a simple trick to improve the approximation ratio under certain conditions.