The interplay between big data and sparsity in systems identification

Abstract

Recent advances in distributed control, coupled with an exponential growth in data gathering capabilities, have made feasible a wide range of applications with potential to profoundly impact society, from safer self-aware environments and smart cities to enhanced model-based medical therapies. Yet, achieving this vision requires addressing the challenge of handling large amounts of very high dimensional data. In this chapter, we provide a tutorial showing how to exploit the inherent sparsity of the data, which is present in a large class of identification problems, to overcome the “curse of dimensionality”. The concepts presented here extend traditional ideas from machine learning linking big data and sparsity, to challenging dynamic settings. In particular, we explore the connections between system identification and information extraction from large data sets, using as an example human activity analysis from video data.