  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 data description .
