Dimension reduction for outlier detection using DOBIN

 

This paper introduces DOBIN, a new approach to select a set of basis vectors tailored for outlier detection. DOBIN has a solid mathematical foundation and can be used as a dimension reduction tool for outlier detection tasks. We demonstrate the effectiveness of DOBIN on an extensive data repository, by comparing the performance of outlier detection methods using DOBIN and other bases. We further illustrate the utility of DOBIN as an outlier visualization tool.

R implementation

Python implementation

Cite

@article{dobin,
  author  = {Sevvandi Kandanaarachchi and Rob J Hyndman},
  doi     = {10.1080/10618600.2020.1807353},
  journal = {J Computational \& Graphical Statistics},
  number  = 1,
  pages   = {204-219},
  title   = {Dimension reduction for outlier detection using {DOBIN}},
  url     = {http://robjhyndman.com/publications/dobin},
  volume  = 30,
  year    = 2021
}