Fast Multidimensional Entropy Estimation by k -d Partitioning


We describe a non-parametric estimator for the differential entropy of a multidimensional distribution, given a limited set of data points, by a recursive rectilinear partitioning. The estimator uses an adaptive partitioning method and runs in Θ ( N log N ) time, with low memory requirements. In experiments using known distributions, the estimator is several orders of magnitude faster than other estimators, with only modest increase in bias and variance.

DOI: 10.1109/LSP.2009.2017346

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@article{Stowell2009FastME, title={Fast Multidimensional Entropy Estimation by k -d Partitioning}, author={Dan Stowell and Mark D. Plumbley}, journal={IEEE Signal Process. Lett.}, year={2009}, volume={16}, pages={537-540} }