Learning time varying graphs
We consider the problem of inferring the hidden structure of high-dimensional time-varying data. In particular, we aim at capturing the dynamic relationships by representing data as valued nodes in a sequence of graphs. Our approach is motivated by the observation that imposing a meaningful graph topology can help solving the generally ill-posed and challenging problem of structure inference. To capture the temporal evolution in the sequence of graphs, we introduce a new prior that asserts that the graph edges change smoothly in time. We propose a primal-dual optimization algorithm that scales linearly with the number of allowed edges and can be easily parallelized. Our new algorithm is shown to outperform standard graph learning and other baseline methods both on a synthetic and a real dataset.