Mahalanobis centroidal Voronoi tessellations

@article{Richter2015MahalanobisCV,
  title={Mahalanobis centroidal Voronoi tessellations},
  author={Ronald Richter and Marc Alexa},
  journal={Computers & Graphics},
  year={2015},
  volume={46},
  pages={48-54}
}
Anisotropic centroidal Voronoi tessellations (CVT) are a useful tool for segmenting surfaces in geometric modeling. We present a new approach to anisotropic CVT, where the local distance metric is learned from the embedding of the shape. Concretely, we define the distance metric implicitly as the minimizer of the CVT energy. Constraining the metric tensors to have unit determinant leads to the optimal distance metric being the inverse covariance matrix of the data (i.e. Mahalanobis distances… CONTINUE READING

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