Latent variable graphical model selection via convex optimization
Top Cited Papers
Open Access
- 1 August 2012
- journal article
- Published by Institute of Mathematical Statistics in The Annals of Statistics
- Vol. 40 (4), 1935-1967
- https://doi.org/10.1214/11-aos949
Abstract
Suppose we observe samples of a subset of a collection of random variables. No additional information is provided about the number of latent variables, nor of the relationship between the latent and observed variables. Is it possible to discover the number of latent components, and to learn a statistical model over the entire collection of variables? We address this question in the setting in which the latent and observed variables are jointly Gaussian, with the conditional statistics of the observed variables conditioned on the latent variables being specified by a graphical model. As a first step we give natural conditions under which such latent-variable Gaussian graphical models are identifiable given marginal statistics of only the observed variables. Essentially these conditions require that the conditional graphical model among the observed variables is sparse, while the effect of the latent variables is “spread out” over most of the observed variables. Next we propose a tractable convex program based on regularized maximum-likelihood for model selection in this latent-variable setting; the regularizer uses both the $\ell_{1}$ norm and the nuclear norm. Our modeling framework can be viewed as a combination of dimensionality reduction (to identify latent variables) and graphical modeling (to capture remaining statistical structure not attributable to the latent variables), and it consistently estimates both the number of latent components and the conditional graphical model structure among the observed variables. These results are applicable in the high-dimensional setting in which the number of latent/observed variables grows with the number of samples of the observed variables. The geometric properties of the algebraic varieties of sparse matrices and of low-rank matrices play an important role in our analysis.
Keywords
Other Versions
This publication has 25 references indexed in Scilit:
- High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergenceElectronic Journal of Statistics, 2011
- Stability selectionJournal of the Royal Statistical Society Series B: Statistical Methodology, 2009
- Sparsistency and rates of convergence in large covariance matrix estimationThe Annals of Statistics, 2009
- A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysisBiostatistics, 2009
- Exact Matrix Completion via Convex OptimizationFoundations of Computational Mathematics, 2009
- Covariance regularization by thresholdingThe Annals of Statistics, 2008
- High dimensional covariance matrix estimation using a factor modelJournal of Econometrics, 2008
- Sparse permutation invariant covariance estimationElectronic Journal of Statistics, 2008
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solutionCommunications on Pure and Applied Mathematics, 2006
- Gaussian Markov Distributions over Finite GraphsThe Annals of Statistics, 1986