Machine Learning and Data Mining in Pattern Recognition

Volume 4571 of the series Lecture Notes in Computer Science pp 61-75

Outlier Detection with Kernel Density Functions

  • Longin Jan LateckiAffiliated withCIS Dept. Temple University Philadelphia, PA 19122
  • , Aleksandar LazarevicAffiliated withUnited Technology Research Center 411 Silver Lane, MS 129-15 East Hartford, CT 06108
  • , Dragoljub PokrajacAffiliated withCIS Dept. CREOSA and AMRC, Delaware State University, Dover DE 19901

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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).