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Title: Copula Gaussian graphical models with hidden variables
Authors: Yu, Hang
Dauwels, Justin
Wang, Xueou
Issue Date: 2012
Source: Yu, H., Dauwels, J., & Wang, X. (2012). Copula Gaussian graphical models with hidden variables. 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2177-2180.
Abstract: Gaussian hidden variable graphical models are powerful tools to describe high-dimensional data; they capture dependencies between observed (Gaussian) variables by introducing a suitable number of hidden variables. However, such models are only applicable to Gaussian data. Moreover, they are sensitive to the choice of certain regularization parameters. In this paper, (1) copula Gaussian hidden variable graphical models are introduced, which extend Gaussian hidden variable graphical models to non-Gaussian data; (2) the sparsity pattern of the hidden variable graphical model is learned via stability selection, which leads to more stable results than cross-validation and other methods to select the regularization parameters. The proposed methods are validated on synthetic and real data.
DOI: 10.1109/ICASSP.2012.6288344
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers
SPMS Conference Papers


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