Fast Multidimensional Entropy Estimation by k -d Partitioning

Abstract

—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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A new class of entropy estimators for multidimensional densities

  • E G Learned-Miller
  • 2003
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  • C Arndt
  • 2001
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