Continuous persistence landscapes

Fuente: arXiv
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Main Authors: Zhao, Wanchen, Bubenik, Peter
Format: Preprint
Published: 2025
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author Zhao, Wanchen
Bubenik, Peter
author_facet Zhao, Wanchen
Bubenik, Peter
contents As the size of data increase, persistence diagrams often exhibit structured asymptotic behavior, converging weakly to a Radon measure. However, conventional vector summaries such as persistence landscapes are not well-behaved in this setting, particularly for diagrams with high point multiplicities. We introduce continuous persistence landscapes, a new vectorization defined on a special class of Borel measures, which we call q-tame measures. It includes both the persistence diagrams and their weak limits. Our construction generalizes persistence landscapes to a measure-theoretic setting, preserving the intrinsic structure of persistence measures. We show that this vector summary is bijective and L^1-stable under mild assumptions, and that the original measure can be uniquely reconstructed. This approach gives a more faithful description of the shape of data in the limit and provides a stable, invertible way to analyze topological features in large systems.
format Preprint
id arxiv_https___arxiv_org_abs_2512_00206
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Continuous persistence landscapes
Zhao, Wanchen
Bubenik, Peter
Algebraic Topology
As the size of data increase, persistence diagrams often exhibit structured asymptotic behavior, converging weakly to a Radon measure. However, conventional vector summaries such as persistence landscapes are not well-behaved in this setting, particularly for diagrams with high point multiplicities. We introduce continuous persistence landscapes, a new vectorization defined on a special class of Borel measures, which we call q-tame measures. It includes both the persistence diagrams and their weak limits. Our construction generalizes persistence landscapes to a measure-theoretic setting, preserving the intrinsic structure of persistence measures. We show that this vector summary is bijective and L^1-stable under mild assumptions, and that the original measure can be uniquely reconstructed. This approach gives a more faithful description of the shape of data in the limit and provides a stable, invertible way to analyze topological features in large systems.
title Continuous persistence landscapes
topic Algebraic Topology
url https://arxiv.org/abs/2512.00206