Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data

TLDR

Existing interior point methods for sparse graphical model estimation are computationally prohibitive beyond tens of nodes. The authors aim to develop scalable algorithms for sparse estimation of Gaussian or binary graphical models that can handle thousands of nodes. They formulate a convex l1‑penalized maximum likelihood problem and solve it with block coordinate descent and Nesterov’s first‑order method, extending the framework via a log‑determinant relaxation to binary data and validating on synthetic, gene‑expression, and senate‑voting datasets.

Abstract

We consider the problem of estimating the parameters of a Gaussian or binary distribution in such a way that the resulting undirected graphical model is sparse. Our approach is to solve a maximum likelihood problem with an added l1-norm penalty term. The problem as formulated is convex but the memory requirements and complexity of existing interior point methods are prohibitive for problems with more than tens of nodes. We present two new algorithms for solving problems with at least a thousand nodes in the Gaussian case. Our first algorithm uses block coordinate descent, and can be interpreted as recursive l1-norm penalized regression. Our second algorithm, based on Nesterov's first order method, yields a complexity estimate with a better dependence on problem size than existing interior point methods. Using a log determinant relaxation of the log partition function (Wainwright and Jordan, 2006), we show that these same algorithms can be used to solve an approximate sparse maximum likelihood problem for the binary case. We test our algorithms on synthetic data, as well as on gene expression and senate voting records data.