Joint Signal Recovery and Graph Learning from Incomplete Time-Series
Amirhossein Javaheri Arash Amini Farokh Marvasti Daniel P. Palomar
Learning a graph from data is the key to taking advantage of graph signal
processing tools. Most of the conventional algorithms for graph learning
require complete data statistics, which might not be available in some
scenarios. In this work, we aim to learn a graph from incomplete time-series
observations. From another viewpoint, we consider the problem of semi-blind
recovery of time-varying graph signals where the underlying graph model is
unknown. We propose an algorithm based on the method of block successive
upperbound minimization (BSUM), for simultaneous inference of the signal and
the graph from incomplete data.
  Simulation results on synthetic and real time-series demonstrate the
performance of the proposed method for graph learning and signal recovery.