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Type: Conference paper
Title: Critical values of a kernel density-based mutual information estimator
Author: May, R.
Dandy, G.
Maier, H.
Fernando, T.
Citation: International Joint Conference on Neural Networks, 16-21 July 2006:pp.4898-4903
Publisher: IEEE
Publisher Place: CDROM
Issue Date: 2006
Series/Report no.: IEEE International Joint Conference on Neural Networks (IJCNN)
ISBN: 0780394909
ISSN: 1098-7576
Conference Name: International Joint Conference on Neural Networks (2006 : Vancouver, Canada)
Editor: Yen, G.
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.
Description: Copyright © 2006 IEEE
DOI: 10.1109/IJCNN.2006.247170
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