Workshop
Learning Algebraic Varieties from Samples
- Bernd Sturmfels (Max Planck Institute for Mathematics in the Sciences)
Abstract
Data sets often have an inherent geometric structure, revealed by approximate algebraic relations of low degree. We discuss a range of methods for studying such samples. One aim is to enhance the toolkit of manifold learning and applied topology with the power of algebraic geometry. This is an ongoing joint project with Paul Breiding, Sara Kalisnik and Madeleine Weinstein.
