Assignment Flows for Data Labeling and Pattern Formation on Graphs

Assignment flows are smooth dynamical systems for labeling data on graphs. After introducing the basic approach, I explain a recent extension to unsupervised scenarios, self-assignment flows, that combine computation of prototypes for data coding and data labeling using these prototypes. A brief outlook on parameter learning and the problem to understand their function conclude the talk.