Nerve Models of Subdivision Bifiltrations
Michael Lesnick, Kenneth McCabe

TL;DR
This paper analyzes subdivision bifiltrations in topological data analysis, introduces nerve-based models, and provides approximation methods with size bounds, demonstrating robustness and limitations of existing models.
Contribution
It introduces a nerve-based simplicial model for subdivision bifiltrations and provides new approximation bounds with size guarantees, improving understanding of their robustness and computational complexity.
Findings
The nerve-based model's $k$-skeleton size is $O(m^{k+1})$.
A $ frac{1}{ ext{approximation}}$ of $ ext{SR}(X)$, called $ ext{J}(X)$, has size $O(|X|^{k+2})$.
The $ frac{1}{ ext{approximation}}$ factor of $ frac{1}{ ext{approximation}}$ is tight; no exact poly-size model exists.
Abstract
We study the size of Sheehy's subdivision bifiltrations, up to homotopy. We focus in particular on the subdivision-Rips bifiltration of a metric space , the only density-sensitive bifiltration on metric spaces known to satisfy a strong robustness property. Given a simplicial filtration with a total of maximal simplices across all indices, we introduce a nerve-based simplicial model for its subdivision bifiltration whose -skeleton has size . We also show that the -skeleton of any simplicial model of has size at least . We give several applications: For an arbitrary metric space , we introduce a -approximation to , denoted , whose -skeleton has size . This improves on the previous best approximation bound of , achieved by the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Numerical Analysis Techniques
