AR Identification of Latent-Variable Graphical Models

作者：Zorzi Mattia; Sepulchre Rodolphe

来源：IEEE Transactions on Automatic Control, 2016, 61(9): 2327-2340.

DOI：10.1109/TAC.2015.2491678

• Convex optimization
• convex relaxation
• latent-variable graphical models
• system identification

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.

• 出版日期2016-9