Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
G. Babazadeh Eslamlou, A. Jung, N. Görtz:
"Smooth graph signal recovery via efficient Laplacian solvers";
Vortrag: IEEE Int. Conference on Acoustics, Speech, and Signal Processing (ICASSP),
New Orleans, Usa;
05.03.2017
- 09.03.2017; in: "2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
IEEE,
New Orleans, Usa
(2017),
ISBN: 978-1-5090-4117-6;
S. 5915
- 5919.
Kurzfassung englisch:
We consider the problem of recovering a smooth graph signal from noisy samples observed at a small number of nodes. The signal recovery is formulated as a convex optimization problem using Tikhonov regularization based on the graph Laplacian quadratic form. The optimality conditions for this optimization problem form a system of linear equations involving the graph Laplacian. We solve this linear system via the iterative Gauss-Seidel method, which is shown to be particularly well-suited for smooth graph signal recovery. The effectiveness of the proposed recovery method is verified by numerical experiments using a real-world data-set.
Schlagworte:
Graph Signals, Recovery
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/ICASSP.2017.7953291
Elektronische Version der Publikation:
http://publik.tuwien.ac.at/files/publik_260650.pdf
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.