arXiv · 2412.03806
Dynamical Persistent Homology via Wasserstein Gradient Flow
Abstract
In this study, we introduce novel methodologies designed to adapt original data in response to the dynamics of persistence diagrams along Wasserstein gradient flows. Our research focuses on the development of algorithms that translate variations in persistence diagrams back into the data space. This advancement enables direct manipulation of the data, guided by observed changes in persistence diagrams, offering a powerful tool for data analysis and interpretation in the context of topological data analysis.
Explore related subjects
Keep this discovery
Minghua Wang, Jinhui Xu. 2024-12-05. Dynamical Persistent Homology via Wasserstein Gradient Flow. https://arxiv.org/abs/2412.03806
Cite the original work for its findings. Save a collection to share your selection of sources.