RKOF: robust kernel-based local outlier detection
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Novelty detection using a new group outlier factor
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
A least-squares approach to anomaly detection in static and sequential data
Pattern Recognition Letters
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Outlier detection keeps an important and attractive task of the knowledge discovery in databases. In this paper, a novel approach named Multi-scale Local Kernel Regression is proposed. It transfers the unsupervised learning of outlier detection to the classic non-parameter regression learning. Through preprocessing the original data by the basic local density-based method, it adopts the local kernel regression estimator in the multiple scale neighborhoods to determine outliers. Experiments on several real life data sets demonstrate that this approach is promising in detection performance.