Structure Learning of Deep Gaussian and Non-Gaussian Information Fusion Framework for Automated Predictive Data Analytics.

Abstract

To combine the strengths of Gaussian and non-Gaussian latent variable models, a novel information fusion strategy has recently been proposed under the deep learning framework. Although promising results have been obtained, the critical structure learning problem remains unsolved, which seriously hinders the automation of data-driven modeling and analytics. In this article, the maximal information coefficient (MIC) method is introduced as a measurement of the AS between two latent variables, which has no restriction in the type of data distribution. Through an assessment on the necessity of adding a new hidden layer into the deep model in each step, an evaluation index is defined for automatic determination of the required hidden layers during the model training process. For time-varying industrial production environments, reconfiguration or updating of the model structure is frequently required. In this case, automated data-driven modeling and structure learning can significantly improve the efficiency of data analytics. Based on the study results obtained from two real industrial examples, the proposed structure learning algorithm is feasible, and the automated data analytics scheme has significantly improved the online prediction performance in time-varying industrial processes.