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Talks and Poster Presentations (with Proceedings-Entry):

P. Berger, M. Buchacher, G. Hannak, G. Matz:
"Graph Learning based on Total Variation Minimization";
Poster: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018), Calgary, AB, Canada; 04-15-2018 - 04-20-2018; in: "2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)", IEEE, (2018), 5 pages.



English abstract:
We consider the problem of learning the topology of a graph from a given set of smooth graph signals. We construct a weighted adjacency matrix that best explains the data in the sense of achieving the smallest graph total variation. For the case of noisy measurements of the graph signals we propose a scheme that simultaneously denoises the signals and learns the graph adjacency matrix. Our method allows for a direct control of the number of edges and of the weighted node degree. Numerical experiments demonstrate that our graph learning scheme is well suited for community detection.

Keywords:
Graph Learning, Total Variation, Distributed Optimization


Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_276323.pdf


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