Numerical Analysis Seminar: Shiying Li, UNC-Chapel Hill, Transport transforms for machine learning applications
SAS 4201Data or patterns (e.g., signals and images) emanating from physical sensors often exhibit complicated nonlinear structures in high dimensional spaces, which post challenges in constructing effective models and interpretable machine learning algorithms. When data is generated through deformations of certain templates, transport transforms often linearize data clusters which are non-linear in the original domain. We…