Patrizio Frosini

Group equivariant non-expansive operators (GENEOs) have been introduced a few years ago as mathematical tools for approximating data observers when data are represented by real-valued or vector-valued functions (https://rdcu.be/bP6HV). The use of these operators is based on the assumption that the interpretation of data depends on the geometric properties of the observers. In this talk we will illustrate some recent results in the theory of GENEOs, showing how these operators can make available a new approach to topological data analysis and geometric deep learning.