Partial Bayesian Co-training for Virtual Metrology

Building accurate regression models using limited data is a challenging problem in manufacturing data analysis. In this paper, we study a particular semisupervised learning problem where labeled data are limited, while unlabeled data are plentiful. In these conditions, conventional single-view learning methods are prone to overfitting. To tackle this problem, we develop a novel co-training technique, namely partial Bayesian co-training (PBCT). PBCT scales down the original set of features to create a partial view, and then exploit side information from the partial view to enhance the complete model. The PBCT model also allows integrating domain knowledge to enhance model accuracy. The proposed method is validated with experiments on industrial manufacturing data. The experimental results show that under a reduction of labeled data by up to 50%, a robust estimation is still attainable. This suggests that the PBCT model is a promising solution to a broad spectrum of applications.