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2008

ICA based on a Smooth Estimation of the Differential Entropy

9 years 12 months ago
ICA based on a Smooth Estimation of the Differential Entropy
In this paper we introduce the MeanNN approach for estimation of main information theoretic measures such as differential entropy, mutual information and divergence. As opposed to other nonparametric approaches the MeanNN results in smooth differentiable functions of the data samples with clear geometrical interpretation. Then we apply the proposed estimators to the ICA problem and obtain a smooth expression for the mutual information that can be analytically optimized by gradient descent methods. The improved performance of the proposed ICA algorithm is demonstrated on several test examples in comparison with state-ofthe-art techniques.
Lev Faivishevsky, Jacob Goldberger
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Lev Faivishevsky, Jacob Goldberger
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