A Bayesian Approach to Sparse plus Low rank Network Identification

2015/03/25 by Zorzi, Mattia, Chiuso, Alessandro
#FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Optimization and Control (math.OC)

paper · doi:10.48550/arxiv.1503.07340

Abstract

We consider the problem of modeling multivariate time series with parsimonious dynamical models which can be represented as sparse dynamic Bayesian networks with few latent nodes. This structure translates into a sparse plus low rank model. In this paper, we propose a Gaussian regression approach to identify such a model.

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