GLAD: Learning Sparse Graph Recovery
Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Han Liu, Le Song
Abstract
Recovering sparse conditional independence graphs from data is a fundamental problem in machine learning with wide applications. A popular formulation of the problem is an $\ell_1$ regularized maximum likelihood estimation. Many convex optimization algorithms have been designed to solve this formulation to recover the graph structure. Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix. However, it is a challenging task in this case, since the symmetric positive definiteness (SPD) and sparsity of the matrix are not easy to enforce in learned algorithms, and a direct mapping from data to precision matrix may contain many parameters. We propose a deep learning architecture, GLAD, which uses an Alternating Minimization (AM) algorithm as our model inductive bias, and learns the model parameters via supervised learning. We show that GLAD learns a very compact and effective model for recovering sparse graphs from data.
BibTeX
@inproceedings{
Shrivastava2020GLAD:,
title={GLAD: Learning Sparse Graph Recovery},
author={Harsh Shrivastava and Xinshi Chen and Binghong Chen and Guanghui Lan and Srinivas Aluru and Han Liu and Le Song},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=BkxpMTEtPB}
}