Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
A. Jung, P. Berger, G. Hannak, G. Matz:
"Scalable graph signal recovery for big data over networks";
Poster: SPAWC 2016 - The 17th International Workshop on Signal Processing Advances in Wireless Communications,
Edinburgh;
03.07.2016
- 06.07.2016; in: "Proc. of IEEE SPAWC 2016",
(2016),
S. 1
- 6.
Kurzfassung englisch:
We formulate the recovery of a graph signal from noisy samples taken on a subset of graph nodes as a convex optimization problem that balances the empirical error for explaining the observed values and a complexity term quantifying the smoothness of the graph signal. To solve this optimization problem, we propose to combine the alternating direction method of multipliers with a novel denoising method that minimizes total variation. Our algorithm can be efficiently implemented in a distributed manner using message passing and thus is attractive for big data problems over networks.
Schlagworte:
Big data;Biological system modeling;Message passing;Noise measurement;Noise reduction;Optimization;TV;ADMM;big data;graph signal recovery;message passing;total variation
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/SPAWC.2016.7536869
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.