Abstract

A new framework for histogram-based mutual information estimation of probability distributions equipped with density functions in (Rd,B(Rd)) is presented in this work. A general histogram-based estimate is proposed, considering nonproduct data-dependent partitions, and sufficient conditions are stipulated to guarantee a strongly consistent estimate for mutual information. Two emblematic families of density-free strongly consistent estimates are derived from this result, one based on statistically equivalent blocks (the Gessaman's partition) and the other, on a tree-structured vector quantization scheme.

Date

March 18, 2010

Authors

Jorge Silva, Shrikanth Narayanan

Journal

IEEE Transactions on Signal Processing

Volume

58

Issue

7

Pages

3497-3511

Publisher

IEEE