Talks and Poster Presentations (with Proceedings-Entry):
P. Berger, M. Buchacher, G. Hannak, G. Matz:
"Graph Learning based on Total Variation Minimization";
Poster: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018),
Calgary, AB, Canada;
- 04-20-2018; in: "2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
We consider the problem of learning the topology of a graph from a given set of smooth graph signals. We construct a weighted adjacency matrix that best explains the data in the sense of achieving the smallest graph total variation. For the case of noisy measurements of the graph signals we propose a scheme that simultaneously denoises the signals and learns the graph adjacency matrix. Our method allows for a direct control of the number of edges and of the weighted node degree. Numerical experiments demonstrate that our graph learning scheme is well suited for community detection.
Graph Learning, Total Variation, Distributed Optimization
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.