Outlier Detection with Kernel Density Functions

Abstract. Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foundation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Outliers are then detected by comparing the local density of each point to the local density of its neighbors. Our experiments performed on several simulated data sets have demonstrated that the proposed approach can outperform two widely used outlier detection algorithms (LOF and LOCI).

• Type: Conference
• Year: 2007
• Where: MLDM
• Authors: Longin Jan Latecki, Aleksandar Lazarevic, Dragoljub Pokrajac
• Keywords: Local Density, Local Density Estimation, Machine Learning, MLDM 2007, Outlier Detection