Sampling Method for Fast Training of Support Vector Data Description

Support Vector Data Description (SVDD) is a popular outlier detection technique, which constructs a flexible description of the input data. SVDD computation time is high for large training datasets, which limits its use in big-data process monitoring applications. We propose a new iterative, sampling-based method for SVDD training. The method incrementally learns the training data description at each iteration by computing SVDD on an independent random sample selected with replacement from the training data set. The experimental results indicate that the proposed method is extremely fast and provides a good quality data description.