Mathematics > Optimization and Control
[Submitted on 30 Apr 2014 (v1), last revised 1 Dec 2014 (this version, v2)]
Title:AR Identification of Latent-variable Graphical Models
View PDFAbstract:The paper proposes an identification procedure for autoregressive gaussian stationary stochastic processes wherein the manifest (or observed) variables are mostly related through 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 decomposition.
Submission history
From: Mattia Zorzi [view email][v1] Wed, 30 Apr 2014 20:33:47 UTC (44 KB)
[v2] Mon, 1 Dec 2014 11:59:47 UTC (94 KB)
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