Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Support Vector Data Description
Machine Learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
An online support vector machine for abnormal events detection
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
One-Class Novelty Detection for Seizure Analysis from Intracranial EEG
The Journal of Machine Learning Research
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Novelty or anomaly detection in spatio/temporal data refers to the automatic identification of novel or abnormal events embedded in data that occur at a specific location/time. Traditional techniques used in process control to identify novelties are not robust for noise in the data set. We present an algorithm based on the support vector machine approach for domain description. This technique is intrinsicly robust for outliers in the data set but to make it work, several extensions are needed which form the contribution of this work: an extended representation of the spatio/temporal data, a tensor product kernel to separately deal with the distinct features of time and measurements, and a voting function which identifies novelties based on different representations of the time series in a robu st way. Experimental results on both artificial and real data demonstrate that our algorithm performs significantly better than other standard techniques used in process control.