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dc.contributor.authorNatali, Alberto
dc.contributor.authorCoutino, Mario
dc.contributor.authorIsufi, Elvin
dc.contributor.authorLeus, Geert
dc.date.accessioned2020-11-04T08:13:09Z
dc.date.available2020-11-04T08:13:09Z
dc.date.issued2020-10-22
dc.identifier.urihttp://hdl.handle.net/10754/665802
dc.description.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.
dc.description.sponsorshipThis work was supported in parts by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700. Mario Coutino is partially supported by CONACYT.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2010.11634
dc.rightsArchived with thanks to arXiv
dc.titleOnline Time-Varying Topology Identification via Prediction-Correction Algorithms
dc.typePreprint
dc.eprint.versionPre-print
dc.contributor.institutionFaculty of Electrical Engineering, Mathematics and Computer Science Delft University of Technology, Delft, The Netherlands.
dc.identifier.arxivid2010.11634
kaust.grant.numberOSR-2015-Sensors-2700
refterms.dateFOA2020-11-04T08:13:39Z
kaust.acknowledged.supportUnitOSR-2015-Sensors-2700


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