vix.ing · top · new · best · stats · spec

Stratified Space Learning: Reconstructing Embedded Graphs

2019/09/27 by Yossi Bokor, Daniel Grixti-Cheng, Bokor, Yossi +7
Computer Science · Mathematics · #Digital Image Processing Techniques #Geochemistry and Geologic Mapping #Topological and Geometric Data Analysis #cs.CG #math.AT #math.GT

paper · pdf · doi:10.48550/arxiv.1909.12474

7 pages, 3 figures, accepted for MODSIM 2019 conference

arxiv created 2019/09/27 · arxiv updated 2019/09/30

Abstract

Many data-rich industries are interested in the efficient discovery and modelling of structures underlying large data sets, as it allows for the fast triage and dimension reduction of large volumes of data embedded in high dimensional spaces. The modelling of these underlying structures is also beneficial for the creation of simulated data that better represents real data. In particular, for systems testing in cases where the use of real data streams might prove impractical or otherwise undesirable. We seek to discover and model the structure by combining methods from topological data analysis with numerical modelling. As a first step in combining these two areas, we examine the recovery of the abstract graph G structure, and model a linear embedding |G| given only a noisy point cloud sample X of |G|.

Related