A One-Class Kernel Fisher Criterion for Outlier Detection

@article{Dufrenois2015AOK,
  title={A One-Class Kernel Fisher Criterion for Outlier Detection},
  author={Franck Dufrenois},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2015},
  volume={26},
  pages={982-994}
}
Recently, Dufrenois and Noyer proposed a one class Fisher's linear discriminant to isolate normal data from outliers. In this paper, a kernelized version of their criterion is presented. Originally on the basis of an iterative optimization process, alternating between subspace selection and clustering, I show here that their criterion has an upper bound making these two problems independent. In particular, the estimation of the label vector is formulated as an unconstrained binary linear… CONTINUE READING
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