Graphical Model Selection for Gaussian Conditional Random Fields in the Presence of Latent Variables.

ABSTRACT: We consider the problem of learning a conditional Gaussian graphical model in the presence of latent variables. Building on recent advances in this field, we suggest a method that decomposes the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive convergence bounds for this estimator and show that it is well-behaved in the high-dimensional regime as well as "sparsistent" (i.e., capable of recovering the graph structure). We then show how proximal gradient algorithms and semi-definite programming techniques can be employed to fit the model to thousands of variables. Through extensive simulations, we illustrate the conditions required for identifiability and show that there is a wide range of situations in which this model performs signif

SUBMITTER: Frot B 

PROVIDER: S-EPMC6636895 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature