Authors | R. Money, J. Krishnan, B. Beferull-Lozano and E. Isufi |
Title | Scalable and Privacy-aware Online Learning of Nonlinear Structural Equation Models |
Afilliation | Machine Learning |
Project(s) | Signal and Information Processing for Intelligent Systems |
Status | Accepted |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | IEEE Open Journal of Signal Processing |
Publisher | IEEE |
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. \E{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. |
Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway. |
DOI | 10.36227/techrxiv.21407952.v1 |
Citation Key | 42845 |