B1994
Title: Densely connected sub-gaussian linear structural equation model learning via l1- and l2-regularized regressions
Authors: Semin Choi - University of Seoul (Korea, South) [presenting]
Gunwoong Park - Seoul National University (Korea, South)
Abstract: A new algorithm is developed for learning densely connected sub-Gaussian linear structural equation models (SEMs) in high-dimensional settings, where the number of nodes increases with an increasing number of samples. The proposed algorithm consists of two main steps: (i) the component-wise ordering estimation using L2-regularized regression and (ii) the presence of edge estimation using L1-regularized regression. Hence, the proposed algorithm can recover a large degree graph with a small degree constraint.
