Talks and Poster Presentations (with Proceedings-Entry):
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,
- 07-06-2016; in: "Proc. of IEEE SPAWC 2016",
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.
Big data;Biological system modeling;Message passing;Noise measurement;Noise reduction;Optimization;TV;ADMM;big data;graph signal recovery;message passing;total variation
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
Created from the Publication Database of the Vienna University of Technology.