Incremental Data-Driven Topology Learning for Time-Varying Graph Signals

In this paper the topology learning for time-varying graph signals with incremental data is studied. In order to learn the topology which is slowly time-varying during data collection, we separate the data of observation into multiple groups by time, and for each group we model the topology learning as a sparse optimization problem, in which the penalty function is designed to consider both the incremental data and previous topology information for graph learning. Moreover, a correction function for dynamic topology is developed by considering a priori information of topology changes. Based on that, by solving the optimization problem we then propose a dynamic topology learning and tracking algorithm to learn as well as track the varying graph topology. Simulations on synthetic and real-world dataset are performed to reveal the performance gain of the proposed algorithm.