Local Outlier Detection Based on Kernel Regression

Jun Gao, Weiming Hu, Wei Li, Zhongfei Mark Zhang, Ou Wu

Abstract: 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.