AR Identification of Latent-Variable Graphical Models
Accepted version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Zorzi, M
Sepulchre, Rodolphe 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
1558-2523
Volume Title
61
Publisher
IEEE