Learning Hierarchies from ICA Mixtures

Abstract

This paper presents a novel procedure to classify data from mixtures of independent component analyzers. The procedure includes two stages: learning the parameters of the mixtures (basis vectors and bias terms) and clustering the ICA mixtures following a bottom-up agglomerative scheme to construct a hierarchy for classification. The approach for the estimation of the source probability density function is non-parametric and the minimum kullback-Leibler distance is used as a criterion for merging clusters at each level of the hierarchy. Validation of the proposed method is presented from several simulations including ICA mixtures with uniform and Laplacian source distributions and processing real data from impact-echo testing experiments.