Random Feature Approximation for Online Nonlinear Graph Topology Identification
Random Feature Approximation for Online Nonlinear Graph Topology Identification
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 …