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; 03-05-2017 - 03-09-2017; 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.

Graph Signals, Recovery

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.