A Nearest-Neighbor Approach to Estimating Divergence between Continuous Random Vectors

@article{Wang2006ANA,
  title={A Nearest-Neighbor Approach to Estimating Divergence between Continuous Random Vectors},
  author={Qing Yuan Wang and S. Kulkarni and Sergio Verd{\'u}},
  journal={2006 IEEE International Symposium on Information Theory},
  year={2006},
  pages={242-246}
}
A method for divergence estimation between multidimensional distributions based on nearest neighbor distances is proposed. Given i.i.d. samples, both the bias and the variance of this estimator are proven to vanish as sample sizes go to infinity. In experiments on high-dimensional data, the nearest neighbor approach generally exhibits faster convergence compared to previous algorithms based on partitioning 
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