Authors | R. Money, J. Krishnan, B. Beferull-Lozano and E. Isufi |
Title | Online Edge Flow Imputation on Networks |
Afilliation | Machine Learning |
Project(s) | Signal and Information Processing for Intelligent Systems |
Status | Published |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | IEEE Signal Processing Letters |
Publisher | IEEE |
Abstract | A novel online algorithm for missing data imputation for networks with signals defined on the edges is presented in this paper. Leveraging the prior knowledge intrinsic to most real-world networks, we propose a bi-level optimization scheme that includes (i) a sparse line graph identification strategy by solving a group-Lasso-based optimization framework via composite objective mirror descent to exploit the causal dependencies among the signals and (ii) a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation to exploit the flow conservation. To the best of our knowledge, this is the first SC-based attempt for time-varying signal imputation, whose advantages have been demonstrated through numerical experiments conducted using EPANET models of both synthetic and real water distribution networks. |
Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 and the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway. |
DOI | 10.1109/LSP.2022.3221846 |
Citation Key | 42575 |