dc.contributor.author | Money, Rohan Thekkemarickal | |
dc.contributor.author | Krishnan, Joshin Parakkulangarayil | |
dc.contributor.author | Beferull-Lozano, Baltasar | |
dc.contributor.author | Isufi, Elvin | |
dc.date.accessioned | 2024-04-29T06:33:32Z | |
dc.date.available | 2024-04-29T06:33:32Z | |
dc.date.created | 2023-06-14T14:08:27Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Money, R. T., Krishnan, J. P., Beferull-Lozano, B. & Isufi, E. (2023). Scalable and Privacy-Aware Online Learning of Nonlinear Structural Equation Models. IEEE Open Journal of Signal Processing, 4, s. 61-70. | en_US |
dc.identifier.issn | 2644-1322 | |
dc.identifier.uri | https://hdl.handle.net/11250/3128296 | |
dc.description.abstract | An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation. The online learning strategy uses a group-lasso-based optimization framework with a prediction-corrections technique that accounts for the model evolution. The proposed approach has three properties of interest. First, it enjoys node-separable learning, which allows for scalability in large networks. Second, it offers privacy in SEM learning by replacing the actual data with node-specific random features. Third, its performance can be characterized theoretically via a dynamic regret analysis, showing that it is possible to obtain a linear dynamic regret bound under mild assumptions. Numerical results with synthetic and real data corroborate our findings and show competitive performance w.r.t. state-of-the-art alternatives. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Scalable and Privacy-Aware Online Learning of Nonlinear Structural Equation Models | en_US |
dc.title.alternative | Scalable and Privacy-Aware Online Learning of Nonlinear Structural Equation Models | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © 2023 The Author(s) | en_US |
dc.subject.nsi | VDP::Teknologi: 500 | en_US |
dc.subject.nsi | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 | en_US |
dc.source.pagenumber | 61-70 | en_US |
dc.source.volume | 4 | en_US |
dc.source.journal | IEEE Open Journal of Signal Processing | en_US |
dc.identifier.doi | https://doi.org/10.1109/OJSP.2023.3241580 | |
dc.identifier.cristin | 2154534 | |
dc.relation.project | Norges forskningsråd: 270730 | en_US |
dc.relation.project | Universitetet i Agder: Wisenet | en_US |
cristin.qualitycode | 1 | |