@inproceedings{ec5c2eca7bf94cbcb1bcb2c8f9e6952c,
title = "Learning time varying graphs",
abstract = "We consider the problem of inferring the hidden structure of high-dimensional time-varying data. In particular, we aim at capturing the dynamic relationships by representing data as valued nodes in a sequence of graphs. Our approach is motivated by the observation that imposing a meaningful graph topology can help solving the generally ill-posed and challenging problem of structure inference. To capture the temporal evolution in the sequence of graphs, we introduce a new prior that asserts that the graph edges change smoothly in time. We propose a primal-dual optimization algorithm that scales linearly with the number of allowed edges and can be easily parallelized. Our new algorithm is shown to outperform standard graph learning and other baseline methods both on a synthetic and a real dataset.",
keywords = "Graph learning, covariance estimation, graph quality, network inference, time varying graph",
author = "Vassilis Kalofolias and Andreas Loukas and Dorina Thanou and Pascal Frossard",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952672",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "2826--2830",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
address = "United States",
}