2025/06/17 by Kapulkin, Chris, Kershaw, Nathan · 1 citation
#05C90 #55U05 #62R40 #68T09 #Algebraic Topology (math.AT) #Combinatorics (math.CO) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Statistics Theory (math.ST)
paper · doi:10.48550/arxiv.2506.15020
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.