Diagnostic Sparse Connectivity Networks with Regularization Template

Real-world dynamic systems and complex objects are often monitored with multivariate time series where each dimension represents a system signal. Performing accurate diagnostic for a group of dynamic systems while simultaneously taking into account their similarities/distinctions, is a non-trivial task. In this paper, we develop an adaptive regularization approach to learning sparse connectivity structures in complex dynamic systems. The learned connectivity networks shed lights on the structural compositions of the system and hence can serve as highly informative inputs for various machine learning tasks. In particular, we focus on high-dimensional and semi-supervised learning scenarios and present a joint learning method to recover system-wise connectivity patterns by adaptively constructing a shared, sparsity-inducing regularization template across all systems. The shared template can be intuitively interpreted and used as a modeling template for efficient analysis of new systems. Moreover, our approach can flexibly incorporate information such as must-links and cannot-links for constructing regularization templates. Overall, our approach, named sparse adaptive regularization (SAR), can extract structure-related connectivity features efficiently and effectively, and result in significant improvements for machine learning tasks in dynamic systems. We benchmark our approach against the state-of-the-art methods with real-world data. Our results demonstrate the superiority of our approach over the baselines in terms of accuracy, efficiency, and interpretability.