Multivariate and functional classification using depth and distance

@article{Hubert2017MultivariateAF,
  title={Multivariate and functional classification using depth and distance},
  author={Mia Hubert and Peter J. Rousseeuw and Pieter Segaert},
  journal={Advances in Data Analysis and Classification},
  year={2017},
  volume={11},
  pages={445-466}
}
We construct classifiers for multivariate and functional data. Our approach is based on a kind of distance between data points and classes. The distance measure needs to be robust to outliers and invariant to linear transformations of the data. For this purpose we can use the bagdistance which is based on halfspace depth. It satisfies most of the properties of a norm but is able to reflect asymmetry when the class is skewed. Alternatively we can compute a measure of outlyingness based on the… 
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