Learning sparse linear dynamic networks in a hyper-parameter free setting
This work addresses the challenge of network estimation in dynamic systems for researchers in signal processing or network analysis, but it appears incremental as it builds on existing iterative frameworks like SPICE.
The authors tackled the problem of estimating the topology and dynamics of sparse linear dynamic networks without requiring hyperparameter tuning, proposing a method that is computationally efficient and applicable under varying noise conditions, with numerical experiments demonstrating its usefulness.
We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting. We propose a method to estimate the network dynamics in a computationally efficient and parameter tuning-free iterative framework known as SPICE (Sparse Iterative Covariance Estimation). The estimated dynamics directly reveal the underlying topology. Our approach does not assume that the network is undirected and is applicable even with varying noise levels across the modules of the network. We also do not assume any explicit prior knowledge on the network dynamics. Numerical experiments with realistic dynamic networks illustrate the usefulness of our method.