AR Identification of Latent-Variable Graphical Models
IEEE Transactions on Automatic Control
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Zorzi, M., & Sepulchre, R. (2016). AR Identification of Latent-Variable Graphical Models. IEEE Transactions on Automatic Control, 61 (9), 2327-2340. https://doi.org/10.1109/TAC.2015.2491678
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.
convex optimization, convex relaxation, latent-variable graphical models, system identification
External DOI: https://doi.org/10.1109/TAC.2015.2491678
This record's URL: https://www.repository.cam.ac.uk/handle/1810/261405