Identification of Sparse Reciprocal Graphical Models
Optimization and Control
2018-06-13 v1
Abstract
In this paper we propose an identification procedure of a sparse graphical model associated to a Gaussian stationary stochastic process. The identification paradigm exploits the approximation of autoregressive processes through reciprocal processes in order to improve the robustness of the identification algorithm, especially when the order of the autoregressive process becomes large. We show that the proposed paradigm leads to a regularized, circulant matrix completion problem whose solution only requires computations of the eigenvalues of matrices of dimension equal to the dimension of the process.
Cite
@article{arxiv.1806.04423,
title = {Identification of Sparse Reciprocal Graphical Models},
author = {Daniele Alpago and Mattia Zorzi and Augusto Ferrante},
journal= {arXiv preprint arXiv:1806.04423},
year = {2018}
}