Robust Sparse Gaussian Process Regression for Soft Sensing in Industrial Big Data Under the Outlier Condition

The presence of outliers in the training data affects the accuracy of the constructed model. To cope with the outlier interference in the model construction process, some robust methods have been proposed on the basis of the nonparametric method, Gaussian process regression (GPR), without eliminating the outliers previously. However, the high complexity of these robust GPR methods makes them unable to cope with situations where the amount of data is too large. In this article, we analyze the impact of outliers on model construction in the setting of big data and propose a robust version based on the sparse GPR. Empirical evaluations conducted on two publicly available datasets, as well as a nitrogen oxides soft sensor designed for a physical diesel engine whose data exist outliers that are difficult to distinguish from normal data, provide compelling evidence to support the notion that the proposed method leads to significant enhancements in performance.