Critical Values of a Kernel Density-based Mutual Information Estimator
May, R.J.
Dandy, G.C.
Maier, H.R.
Fernando, T.M.K.
Univ. of Adelaide, Adelaide;
This paper appears in: Neural Networks, 2006. IJCNN '06. International Joint Conference on
Publication Date: 0-0 0
On page(s): 4898-4903
Location: Vancouver, BC,
ISBN: 0-7803-9490-9
INSPEC Accession Number: 9723399
Digital Object Identifier: 10.1109/IJCNN.2006.247170
Current Version Published: 2006-10-30
Abstract
Recently, mutual information (MI) has become widely recognized as a statistical measure of dependence that is suitable for applications where data are non-Gaussian, or where the dependency between variables is non-linear. However, a significant disadvantage of this measure is the inability to define an analytical expression for the distribution of MI estimators, which are based upon a finite dataset. This paper deals specifically with a popular kernel density based estimator, for which the distribution is determined empirically using Monte Carlo simulation. The application of the critical values of MI derived from this distribution to a test for independence is demonstrated within the context of a benchmark input variable selection problem.
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