In this paper, we consider the simplest class of stratified spaces -- linearly embedded graphs. We present an algorithm that learns the abstract structure of an embedded graph and models the specific embedding from a point cloud sampled from it. We use tools and inspiration from computational geometry, algebraic topology, and topological data analysis and prove the correctness of the identified abstract structure under assumptions on the embedding. The algorithm is implemented in the Julia package http://github.com/yossibokor/Skyler.jl , which we used for the numerical simulations in this paper.
@article{arxiv.2101.04375,
title = {Towards Stratified Space Learning: Linearly Embedded Graphs},
author = {Yossi Bokor and Katharine Turner and Christopher Williams},
journal= {arXiv preprint arXiv:2101.04375},
year = {2023}
}