摘要
This paper investigates the problem of modelling regression with labelled and unlabeled data samples commonly found in industrial processes. By incorporating additional information on unlabeled data samples, a new semi-supervised latent factor analysis model is developed. Compared to the purely supervised regression models which can only use the information of labeled dataset, the semi-supervised model can efficiently extract useful information for improvement of the regression performance. Furthermore, the proposed basic semi-supervised model has been extended to a mixture form, which is capable of describing data from more complex processes. For predictive modeling for key/mass variables, two soft sensors are constructed based on the semi-supervised models. Then, three case studies are used to evaluate the performance of the proposed soft sensing methods.