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AR Identification of Latent-Variable Graphical Models

Accepted version
Peer-reviewed

Type

Article

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Authors

Zorzi, M 
Sepulchre, Rodolphe  ORCID logo  https://orcid.org/0000-0002-7047-3124

Abstract

The paper proposes an identification procedure for autoregressive Gaussian stationary stochastic processes under the assumption that the manifest (or observed) variables are nearly independent when conditioned on 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 decompositions.

Description

Keywords

convex optimization, convex relaxation, latent-variable graphical models, system identification

Journal Title

IEEE Transactions on Automatic Control

Conference Name

Journal ISSN

0018-9286
1558-2523

Volume Title

61

Publisher

IEEE