Title: DREiMac: Dimensionality Reduction with Eilenberg-MacLane Coordinates
Abstract: Dimensionality reduction is the machine learning problem of taking a data set whose elements are described with potentially many features (e.g., the pixels in an image), and computing representations which are as economical as possible (i.e., with few coordinates). In this talk, I will present a framework to leverage the topological structure of data (measured via persistent cohomology) and construct low dimensional coordinates in (classifying) spaces consistent with the underlying data topology.
Here is the zoom link is: https://wse.zoom.us/j/95448608570