Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
G. Matz, T. Dittrich:
"Learning Signed Graphs from Data";
Vortrag: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
Barcelona;
04.05.2020
- 08.05.2020; in: "ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
IEEE,
(2020),
5 S.
Kurzfassung englisch:
Signed graphs have recently been found to offer advantages over unsigned graphs in a variety of tasks. However, the problem of learning graph topologies has only been considered for the unsigned case. In this paper, we propose a conceptually simple and flexible approach to signed graph learning via signed smoothness metrics. Learning the graph amounts to solving a convex optimization problem, which we show can be reduced to an efficiently solvable quadratic problem. Applications to signal reconstruction and clustering corroborate the effectiveness of the proposed method.
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/ICASSP40776.2020.9053083
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.