AdaPtive Noisy Data Augmentation (PANDA) for Simultaneous Construction of Multiple Graph Models

Abstract

We extend the data augmentation technique PANDA by Li et al. (2018) that regularizes single graph estimation to jointly learning multiple graphical models with various node types in a unified framework. We design two types of noise to augment the observed data: the first type regularizes the estimation of each graph while the second type promotes either the structural similarity, referred as the \joint group lasso regularization, or the numerical similarity, referred as the joint fused ridge regularization, among the edges in the same position across graphs. The computation in PANDA is straightforward and only involves obtaining maximum likelihood estimator in generalized linear models in an iterative manner. The simulation studies demonstrate PANDA is non-inferior to existing joint estimation approaches for Gaussian graphical models, and significantly improves over the naive differencing approach for non-Gaussian graphical models. We apply PANDA to a real-life lung cancer microarray data to simultaneously construct four protein networks.

References in corpus (5)

• Extended Bayesian Information Criteria for Gaussian Graphical Models
• Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions
• Stable Graphical Model Estimation with Random Forests for Discrete, Continuous, and Mixed Variables
• Structure estimation for mixed graphical models in high-dimensional data
• PANDA: AdaPtive Noisy Data Augmentation for Regularization of Undirected Graphical Models

Cited by in corpus (1)

• PANDA: AdaPtive Noisy Data Augmentation for Regularization of Undirected Graphical Models