Outlier Detection with Kernel Density Functions

  • Longin Jan Latecki
  • Aleksandar Lazarevic
  • Dragoljub Pokrajac
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4571)


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).


False Alarm Rate Outlier Detection Local Density Estimate Neighbor Query Variable Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Longin Jan Latecki
    • 1
  • Aleksandar Lazarevic
    • 2
  • Dragoljub Pokrajac
    • 3
  1. 1.CIS Dept. Temple University Philadelphia, PA 19122USA
  2. 2.United Technology Research Center 411 Silver Lane, MS 129-15 East Hartford, CT 06108USA
  3. 3.CIS Dept. CREOSA and AMRC, Delaware State University, Dover DE 19901USA

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