Lifelong Bayesian Learning Machines for Streaming Industrial Big Data
Zeyu Yang, Junhua Zheng, Zhiqiang Ge
Abstract
With the advent of the big data era and the timeliness requirements of data processing, a large amount of streaming industrial big data is continuously obtained in real time. Facing this kind of flowing and time-varying knowledge information form, incremental learning is necessary. Lifelong learning (LL), as a typical incremental learning method, can continuously retain and accumulate old knowledge while learning new knowledge, which is very suitable for streaming industrial big data scenarios. At the same time, Bayesian nonparametric (BNP) models can adjust the complexity of the model based on observation data. Motivated by ideas of BNP and LL, a lifelong Bayesian learning machines framework is proposed in this article, which includes model expansion and model optimization. In general, this framework not only learns new effective knowledge and accumulates knowledge through incremental variational Bayesian under model expansion but also uses optimization steps to avoid model degradation caused by unnecessary component information. As an example, Dirichlet processes Gaussian mixture regression (DPGMR) is utilized for process modeling under this framework. To evaluate the feasibility and efficiency of the developed method, a synthetic and a real industrial case are demonstrated.