Partial Gaussian Graphical Model Estimation
This work addresses a domain-specific problem in statistical modeling for high-dimensional data analysis, presenting an incremental improvement over existing methods.
The paper tackles the problem of estimating partial Gaussian graphical models from high-dimensional data by developing a convex formulation using ℓ1-regularized maximum-likelihood estimation, which shows competitive empirical performance in experiments on synthetic and real datasets.
This paper studies the partial estimation of Gaussian graphical models from high-dimensional empirical observations. We derive a convex formulation for this problem using $\ell_1$-regularized maximum-likelihood estimation, which can be solved via a block coordinate descent algorithm. Statistical estimation performance can be established for our method. The proposed approach has competitive empirical performance compared to existing methods, as demonstrated by various experiments on synthetic and real datasets.