Learning Sheaf Laplacians from Smooth Signals

Cellular sheaves are a mathematical structure specifying consistency relations for data associated to vertices and edges of a graph, generalizing connection graphs and matrix weighted graphs. We consider the problem of learning such a sheaf from a collection of highly consistent or smooth signals associated to the vertices of the underlying graph.