Data analysis using discrete cubical homology
Algebraic Topology
2025-06-23 v1 Machine Learning
Combinatorics
Statistics Theory
Statistics Theory
Abstract
We present a new tool for data analysis: persistence discrete homology, which is well-suited to analyze filtrations of graphs. In particular, we provide a novel way of representing high-dimensional data as a filtration of graphs using pairwise correlations. We discuss several applications of these tools, e.g., in weather and financial data, comparing them to the standard methods used in the respective fields.
Cite
@article{arxiv.2506.15020,
title = {Data analysis using discrete cubical homology},
author = {Chris Kapulkin and Nathan Kershaw},
journal= {arXiv preprint arXiv:2506.15020},
year = {2025}
}
Comments
17 pages; comments welcome