Online Time-Varying Topology Identification Via Prediction-Correction Algorithms
KAUST Grant NumberOSR-2015-Sensors-2700
Preprint Posting Date2020-10-22
Permanent link to this recordhttp://hdl.handle.net/10754/665802
MetadataShow full item record
AbstractSignal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of the problem (e.g., water supply networks, power grids), the topology has to be learned from data. Topology identification is a challenging task, as the problem is often ill-posed, and becomes even harder when the graph structure is time-varying. In this paper, we address the problem of dynamic topology identification by building on recent results from time-varying optimization, devising a general-purpose online algorithm operating in non-stationary environments. Because of its iteration-constrained nature, the proposed approach exhibits an intrinsic temporal-regularization of the graph topology without explicitly enforcing it. As a case-study, we specialize our method to the Gaussian graphical model (GGM) problem and corroborate its performance.
CitationNatali, A., Coutino, M., Isufi, E., & Leus, G. (2021). Online Time-Varying Topology Identification Via Prediction-Correction Algorithms. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp39728.2021.9415053
SponsorsThis work was supported in parts by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700. Mario Coutino is partially supported by CONACYT.
Conference/Event nameICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)