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arXiv · 1405.0027

AR Identification of Latent-variable Graphical Models

Mattia Zorzi·Rodolphe Sepulchre
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Abstract

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.

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Mattia Zorzi, Rodolphe Sepulchre. 2014-12-01. AR Identification of Latent-variable Graphical Models. https://arxiv.org/abs/1405.0027

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