Efficient Estimation of Mutual Information for Strongly Dependent Variables

@article{Gao2015EfficientEO,
  title={Efficient Estimation of Mutual Information for Strongly Dependent Variables},
  author={Shuyang Gao and Greg Ver Steeg and Aram Galstyan},
  journal={CoRR},
  year={2015},
  volume={abs/1411.2003}
}
We demonstrate that a popular class of nonparametric mutual information (MI) estimators based on k-nearest-neighbor graphs requires number of samples that scales exponentially with the true MI. Consequently, accurate estimation of MI between two strongly dependent variables is possible only for prohibitively large sample size. This important yet overlooked shortcoming of the existing estimators is due to their implicit reliance on local uniformity of the underlying joint distribution. We… CONTINUE READING
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