Learning the Network Structure of Heterogeneous Data via Pairwise Exponential Markov Random Fields.
Youngsuk Park, David Hallac, Stephen Boyd, Jure Leskovec · PubMed · 2017
, a generalization of the classic graphical lasso problem to heterogeneous domains. To solve this problem, we develop a fast algorithm based on the alternating direction method of multipliers (ADMM). We also prove that our estimator is sparsistent, with guaranteed recovery of the true underlying graphical structure, and that it has a polynomially faster runtime than the current state-of-the-art method for learning such distributions. Experiments on synthetic and real-world examples demonstrate that our approach is both efficient and accurate at uncovering the structure of heterogeneous data.