A least-squares approach to anomaly detection in static and sequential data

@article{Quinn2014ALA,
  title={A least-squares approach to anomaly detection in static and sequential data},
  author={John A. Quinn and Masashi Sugiyama},
  journal={Pattern Recognition Letters},
  year={2014},
  volume={40},
  pages={36-40}
}
We describe a probabilistic, nonparametric method for anomaly detection, based on a squared-loss objective function which has a simple analytical solution. The method emerges from extending recent work in nonparametric leastsquares classification to include a “none-of-the-above” class which models anomalies in terms of non-anamalous training data. The method shares the flexibility of other kernel-based anomaly detection methods, yet is typically much faster to train and test. It can also be… CONTINUE READING
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