Talks and Poster Presentations (with Proceedings-Entry):
G. Babazadeh Eslamlou, A. Jung, N. Görtz:
"Smooth graph signal recovery via efficient Laplacian solvers";
Talk: IEEE Int. Conference on Acoustics, Speech, and Signal Processing (ICASSP),
New Orleans, Usa;
2017-03-05
- 2017-03-09; in: "2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
IEEE,
New Orleans, Usa
(2017),
ISBN: 978-1-5090-4117-6;
5915
- 5919.
English abstract:
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.
Keywords:
Graph Signals, Recovery
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/ICASSP.2017.7953291
Electronic version of the publication:
http://publik.tuwien.ac.at/files/publik_260650.pdf
Created from the Publication Database of the Vienna University of Technology.