4 citations · 8 across the 9 of their papers we have counts for
7 papers · 1 filter
A U-match Algorithm for Persistent Relative Homology
Christian Lentz, Gregory Henselman-Petrusek, Lori Ziegelmeier
A central problem in data-driven scientific inquiry is how to interpret structure in noisy, high-dimensional data. Topological data analysis (TDA) provides a solution via persisten…
A survey of simplicial, relative, and chain complex homology theories for hypergraphs
Ellen Gasparovic, Emilie Purvine, Radmila Sazdanovic +3
Hypergraphs have seen widespread application in network and data science communities in recent years. We present a survey of recent work to construct auxiliary structures from hype…
U-match factorization: sparse homological algebra, lazy cycle representatives, and dualities in persistent (co)homology
Haibin Hang, Chad Giusti, Lori Ziegelmeier +1
Persistent homology is a leading tool in topological data analysis (TDA). Many problems in TDA can be solved via homological -- and indeed, linear -- algebra. However, matrices in…
Minimal Cycle Representatives in Persistent Homology using Linear Programming: an Empirical Study with User's Guide
Lu Li, Connor Thompson, Gregory Henselman-Petrusek +2
Cycle representatives of persistent homology classes can be used to provide descriptions of topological features in data. However, the non-uniqueness of these representatives creat…
Analyzing Collective Motion with Machine Learning and Topology
Dhananjay Bhaskar, Angelika Manhart, Jesse Milzman +4
We use topological data analysis and machine learning to study a seminal model of collective motion in biology [D'Orsogna et al., Phys. Rev. Lett. 96 (2006)]. This model describes…
Local Versus Global Distances for Zigzag Persistence Modules
Ellen Gasparovic, Maria Gommel, Emilie Purvine +4
This short note establishes explicit and broadly applicable relationships between persistence-based distances computed locally and globally. In particular, we show that the bottlen…