Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
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;
15.04.2018
- 20.04.2018; in: "2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
IEEE,
(2018),
5 S.
Kurzfassung englisch:
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.
Schlagworte:
Graph Learning, Total Variation, Distributed Optimization
Elektronische Version der Publikation:
https://publik.tuwien.ac.at/files/publik_276323.pdf
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.