Divergence Estimation for Multidimensional Densities Via $k$-Nearest-Neighbor Distances

@article{Wang2009DivergenceEF,
  title={Divergence Estimation for Multidimensional Densities Via \$k\$-Nearest-Neighbor Distances},
  author={Qing Wang and Sanjeev R. Kulkarni and Sergio Verd{\'u}},
  journal={IEEE Transactions on Information Theory},
  year={2009},
  volume={55},
  pages={2392-2405}
}
A new universal estimator of divergence is presented for multidimensional continuous densities based on k-nearest-neighbor (k-NN) distances. Assuming independent and identically distributed (i.i.d.) samples, the new estimator is proved to be asymptotically unbiased and mean-square consistent. In experiments with high-dimensional data, the k-NN approach generally exhibits faster convergence than previous algorithms. It is also shown that the speed of convergence of the k-NN method can be further… CONTINUE READING

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