A joint convex penalty for inverse covariance matrix estimation

作者：Maurya Ashwini^*

来源：Computational Statistics & Data Analysis, 2014, 75: 15-27.

DOI：10.1016/j.csda.2014.01.015

• Proximal gradient
• Joint penalty
• Convex optimization
• Sparsity

The paper proposes a joint convex penalty for estimating the Gaussian inverse covariance matrix. A proximal gradient method is developed to solve the resulting optimization problem with more than one penalty constraints. The analysis shows that imposing a single constraint is not enough and the estimator can be improved by a trade-off between two convex penalties. The developed framework can be extended to solve wide arrays of constrained convex optimization problems. A simulation study is carried out to compare the performance of the proposed method to graphical lasso and the SPICE estimate of the inverse covariance matrix. Published by Elsevier B.V.

• 出版日期2014-7