AR Identification of Latent-variable Graphical Models
Optimization and Control
2014-12-02 v2
Abstract
The paper proposes an identification procedure for autoregressive gaussian stationary stochastic processes wherein the manifest (or observed) variables are mostly related through a limited number of latent (or hidden) variables. The method exploits the sparse plus low-rank decomposition of the inverse of the manifest spectral density and the efficient convex relaxations recently proposed for such decomposition.
Cite
@article{arxiv.1405.0027,
title = {AR Identification of Latent-variable Graphical Models},
author = {Mattia Zorzi and Rodolphe Sepulchre},
journal= {arXiv preprint arXiv:1405.0027},
year = {2014}
}