SimBa: An Efficient Tool for Approximating Rips-filtration Persistence via Simplicial Batch-collapse

TL;DR

SimBa is a new algorithm that efficiently approximates Rips-filtration persistence in topological data analysis, significantly reducing computational complexity and size of complexes for high-dimensional data.

Contribution

The paper introduces SimBa, a novel algorithm that uses batch collapse and a sparse Rips-like filtration to improve scalability and efficiency in computing persistent homology.

Findings

SimBa achieves significant size reduction of Rips complexes.

SimBa is an order of magnitude faster than existing methods.

The algorithm provides quality guarantees for approximation.

Abstract

In topological data analysis, a point cloud data P extracted from a metric space is often analyzed by computing the persistence diagram or barcodes of a sequence of Rips complexes built on $P$ indexed by a scale parameter. Unfortunately, even for input of moderate size, the size of the Rips complex may become prohibitively large as the scale parameter increases. Starting with the Sparse Rips filtration introduced by Sheehy, some existing methods aim to reduce the size of the complex so as to improve the time efficiency as well. However, as we demonstrate, existing approaches still fall short of scaling well, especially for high dimensional data. In this paper, we investigate the advantages and limitations of existing approaches. Based on insights gained from the experiments, we propose an efficient new algorithm, called SimBa, for approximating the persistent homology of Rips filtrations with quality guarantees. Our new algorithm leverages a batch collapse strategy as well as a new sparse Rips-like filtration. We experiment on a variety of low and high dimensional data sets. We show that our strategy presents a significant size reduction, and our algorithm for approximating Rips filtration persistence is order of magnitude faster than existing methods in practice.