CUED Publications database

AR Identification of Latent-Variable Graphical Models

Zorzi, M and Sepulchre, R (2015) AR Identification of Latent-Variable Graphical Models. IEEE Transactions on Automatic Control, 61. pp. 2327-2340.

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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.

Item Type: Article
Uncontrolled Keywords: convex optimization convex relaxation latent-variable graphical models system identification
Subjects: UNSPECIFIED
Divisions: Div F > Control
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:35
Last Modified: 12 Oct 2017 01:46
DOI: