Diagnostic Sparse Connectivity Networks with Regularization Template

Dynamic systems are often monitored with multivariate time series where each dimension represents a local component measured through a (virtual) sensor. Performing accurate diagnostic for dynamic systems while simultaneously taking into account their similarities/distinctions, is a non-trivial task. To this end, 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 such as classification. In particular, we focus on high-dimensional and semi-supervised learning scenarios and present a joint learning approach to recover system-wise connectivity patterns by adaptively constructing a shared, sparsity-inducing regularization template across all systems. The shared template can be physically interpreted and used as a modeling template for analyzing new systems. Moreover, our approach has the flexibility to incorporate supervising 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.