The past few years have seen an exponential growth in data collection capabilities. Unfortunately, the ability to process this vast amount of data has not kept pace with this growth. Taking full advantage of these increased capabilities requires scalable, computationally efficient algorithms to timely and robustly extract actionable information from the very large data sets generated by the sensors. The goal of this tutorial paper is to illustrate the central role that tools originally developed in the context of systems theory, can play in accomplishing this task. Specifically, we show that many of these problems can be recast into an identification form that exhibit a sparse underlying structure. In turn, this sparsity can be exploited to recast the problem into a convex optimization form that can be efficiently solved with first order methods.
['Octavia I. Camps', 'Constantino M. Lagoa', 'Mario Sznaier']
The role of sparsity and dynamics in extracting information sparsely encoded in very large data sets