dc.contributor.author | Money, Rohan Thekkemarickal | |
dc.contributor.author | Krishnan, Joshin Parakkulangarayil | |
dc.contributor.author | Beferull-Lozano, Baltasar | |
dc.date.accessioned | 2023-04-27T10:52:26Z | |
dc.date.available | 2023-04-27T10:52:26Z | |
dc.date.created | 2022-11-21T16:06:57Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Money, R., Krishnan, J. & Beferull-Lozano, B. (2022). Online Joint Nonlinear Topology Identification and Missing Data Imputation over Dynamic Graphs. European Signal Processing Conference, 687-691. | en_US |
dc.identifier.issn | 2219-5491 | |
dc.identifier.uri | https://hdl.handle.net/11250/3065293 | |
dc.description | Author's accepted manuscript | en_US |
dc.description | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
dc.description.abstract | Extracting causal graph structures from multivariate time series, termed topology identification, is a fundamental problem in network science with several important applications. Topology identification is a challenging problem in real-world sensor networks, especially when the available time series are partially observed due to faulty communication links or sensor failures. The problem becomes even more challenging when the sensor dependencies are nonlinear and nonstationary. This paper proposes a kernel-based online framework using random feature approximation to jointly estimate nonlinear causal dependencies and missing data from partial observations of streaming graph-connected time series. Exploiting the fact that real-world networks often exhibit sparse topologies, we propose a group lasso-based optimization framework for topology identification, which is solved online using alternating minimization techniques. The ability of the algorithm is illustrated using several numerical experiments conducted using both synthetic and real data. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.title | Online Joint Nonlinear Topology Identification and Missing Data Imputation over Dynamic Graphs | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | © 2022 IEEE | en_US |
dc.subject.nsi | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 | en_US |
dc.source.pagenumber | 687-691 | en_US |
dc.source.journal | European Signal Processing Conference | en_US |
dc.identifier.doi | https://doi.org/10.23919/EUSIPCO55093.2022.9909681 | |
dc.identifier.cristin | 2077605 | |
dc.relation.project | IKTPLUSS INDURB: 270730/O70 | en_US |
dc.relation.project | SFI Offshore Mechatronics: 237896/O30 | en_US |
cristin.qualitycode | 1 | |