Sampling real algebraic varieties for topological data analysis

Author: 

Dufresne, E
Edwards, P
Harrington, H
Hauenstein, J

Publication Date: 

17 February 2020

Journal: 

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)

Last Updated: 

2020-08-10T13:03:14.167+01:00

DOI: 

10.1109/ICMLA.2019.00253

page: 

1531-1536

abstract: 

Topological data analysis (TDA) provides tools for computing geometric and topological information about spaces from a finite sample of points. We present an adaptive algorithm for finding provably dense samples of points on real algebraic varieties given a set of defining polynomials for use as input to TDA. The algorithm utilizes methods from numerical algebraic geometry to give formal guarantees about the density of the sampling, and also employs geometric heuristics to reduce the size of the sample. As TDA methods consume significant computational resources that scale poorly in the number of sample points, our sampling minimization makes applying TDA methods more feasible. We provide a software package that implements the algorithm, and showcase it through several examples.

Symplectic id: 

1061289

Submitted to ORA: 

Submitted

Publication Type: 

Conference Paper