Covariance Change Point Detection for Graph Signals

We propose a new approach for covariance change point detection applied to graph signals. Specifically, our method draws on the notion of graph stationarity to derive a relevant parameterization of the covariance matrix that can be used in a cost function. This parameterization allows prior graph knowledge to be incorporated into the detection process and reduces the number of coefficients to be estimated. We have experimentally validated this method against relevant baselines, on synthetic and real data, and showed the influence of several parameters. These experiments demonstrated very low computational complexity, improved robustness against certain adverse effects and competitive performance in more general contexts.