Sampling Method for Fast Training of Support Vector Data Description
This work addresses a bottleneck in outlier detection for big-data process-monitoring applications, though it is incremental as it builds on existing SVDD methods.
The authors tackled the high computational time of Support Vector Data Description (SVDD) for large datasets by proposing an iterative sampling-based method, which significantly speeds up training while maintaining good data description quality.
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 .