Learning Latent Variable Gaussian Graphical Model for Biomolecular Network with Low Sample Complexity.

ABSTRACT: Learning a Gaussian graphical model with latent variables is ill posed when there is insufficient sample complexity, thus having to be appropriately regularized. A common choice is convex ℓ₁ plus nuclear norm to regularize the searching process. However, the best estimator performance is not always achieved with these additive convex regularizations, especially when the sample complexity is low. In this paper, we consider a concave additive regularization which does not require the strong irrepresentable condition. We use concave regularization to correct the intrinsic estimation biases from Lasso and nuclear penalty as well. We establish the proximity operators for our concave regularizations, respectively, which induces sparsity and low rankness. In addition, we extend

SUBMITTER: Wang Y 

PROVIDER: S-EPMC5097857 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature