A Scalable Strategy for the Identification of Latent-Variable Graphical Models

• 成果类型: Article
• 署名作者: Alpago, Daniele; Zorzi, Mattia; Ferrante, Augusto
• 署名单位: University of Padua
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3097558
• 发表日期: 2022
• 页码: 3349-3362
• 关键词: Graphical models; Symmetric matrices; Covariance matrices; manganese; entropy; Stochastic processes; Random variables; Latent-variable graphical models; Maximum entropy; Maximum Likelihood; reciprocal processes; regularization; System identification

摘要

In this article, we propose an identification method for latent-variable graphical models associated with autoregressive (AR) Gaussian stationary processes. The identification procedure exploits the approximation of AR processes through stationary reciprocal processes thus benefiting of the numerical advantages of dealing with block-circulant matrices. These advantages become more and more significant as the order of the process gets large. We show how the identification can be cast in a regularized convex program and we present numerical examples that compares the performances of the proposed method with the existing ones.