2020/03/13 by Yu-Min Chung, Chung, Yu-Min, William Cruse +3 · 1 citation
Computer Science · Mathematics · #Computational Geometry (cs.CG) #FOS: Computer and information sciences #Methodology (stat.ME) #Topological and Geometric Data Analysis #cs.CG #stat.ME
paper · pdf · doi:10.48550/arxiv.2003.06462
arxiv created 2020/03/13 · openalex publication_date 2020/03/13 · arxiv updated 2020/03/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Topological Data Analysis (TDA) is a rising field of computational topology in which the topological structure of a data set can be observed by persistent homology. By considering a sequence of sublevel sets, one obtains a filtration that tracks changes in topological information. These changes can be recorded in multi-sets known as \it persistence diagrams. Converting information stored in persistence diagrams into a form compatible with modern machine learning algorithms is a major vein of research in TDA. \it Persistence curves, a recently developed framework, provides a canonical and flexible way to encode the information presented in persistence diagrams into vectors. In this work, we propose a new set of metrics based on persistence curves. We prove the stability of the proposed metrics. Finally, we apply these metrics to the UCR Time Series Classification Archive. These empirical results show that our metrics perform better than the relevant benchmark in most cases and warrant further study.