Random Feature Approximation for Online Nonlinear Graph Topology Identification

• URL: http://arxiv.org/abs/2110.09935v1
• Date: Tue, 19 Oct 2021 12:48:12 GMT
• Title: Random Feature Approximation for Online Nonlinear Graph Topology Identification
• Authors: Rohan Money, Joshin Krishnan, Baltasar Beferull-Lozano
• Abstract summary: We propose a kernel-based algorithm for graph topology estimation. We exploit the fact that the real-world networks often exhibit sparse topologies. The experiments conducted on real and synthetic data show that the proposed method outperforms its competitors.
• Score: 7.992550355579789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online topology estimation of graph-connected time series is challenging, especially since the causal dependencies in many real-world networks are nonlinear. In this paper, we propose a kernel-based algorithm for graph topology estimation. The algorithm uses a Fourier-based Random feature approximation to tackle the curse of dimensionality associated with the kernel representations. Exploiting the fact that the real-world networks often exhibit sparse topologies, we propose a group lasso based optimization framework, which is solve using an iterative composite objective mirror descent method, yielding an online algorithm with fixed computational complexity per iteration. The experiments conducted on real and synthetic data show that the proposed method outperforms its competitors.

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