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AR Identification of Latent-Variable Graphical Models.
- Source :
- IEEE Transactions on Automatic Control; Sep2016, Vol. 61 Issue 9, p2327-2340, 14p
- Publication Year :
- 2016
-
Abstract
- 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. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 00189286
- Volume :
- 61
- Issue :
- 9
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Automatic Control
- Publication Type :
- Periodical
- Accession number :
- 117759472
- Full Text :
- https://doi.org/10.1109/TAC.2015.2491678