Robust outlier detection using SVM regression

The occurrence of outliers in industrial data is often the rule rather than the exception. Many standard outlier detection methods fail to detect outliers in industrial data because of the high dimensionality of the data. Outlier detection in the case of chemical plant data can be particularly difficult since these data sets are often rank deficient. These problems can be solved by using robust model-based methods that do not require the data to be of full rank. We explore the use of a robust model-based outlier detection approach that makes use of the characteristics of the support vectors obtained by the support vector machine method.