Learning Sheaf Laplacians from Smooth Signals

Jakob Hansen; Robert Ghrist

ICASSP 2019accepted0 citations

Learning Sheaf Laplacians from Smooth Signals

Abstract

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.

BibTeX

@inproceedings{icassp2019_learningsheaflap,
  title = {Learning Sheaf Laplacians from Smooth Signals},
  author = {Jakob Hansen and Robert Ghrist},
  booktitle = {ICASSP 2019},
  year = {2019}
}