ICA based on a Smooth Estimation of the Differential Entropy
Lev Faivishevsky, Jacob H. Goldberger · 2008
In this paper we introduce the MeanNN approach for estimation of main infor-mation 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 inter-pretation. 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-of-the-art techniques. 1