Enhanced Independent Components Analysis (EICA)

Independent Components Analysis through Product Density Estimation

We present a simple direct approach for solving the ICA problem, using density estimation and maximum likelihood. Given a candidate orthogonal frame, we model each of the coordinates using a semi-parametric density estimate based on cubic splines. Since our estimates have two continuous derivatives , we can easily run a second order search for the frame parameters. Our method performs very favo...

Noisy independent component analysis of autocorrelated components.

We present a method for the separation of superimposed, independent, autocorrelated components from noisy multichannel measurement. The presented method simultaneously reconstructs and separates the components, taking all channels into account, and thereby increases the effective signal-to-noise ratio considerably, allowing separations even in the high-noise regime. Characteristics of the measu...

Independent Components Analysis for Signal Separation andDimension

We introduce an entropy maximisation method for extracting K signals from M K mixtures of N M source signals. The mixtures x = (x1; : : : ; xM) T are formed by combining diierent proportions of the independent sources s = (s1;

Independent Components Analysis by Direct Entropy Minimization

This paper presents a new algorithm for the independent components analysis (ICA) problem based on efficient entropy estimates. Like many previous methods, this algorithm directly minimizes the measure of departure from independence according to the estimated Kullback-Leibler divergence between the joint distribution and the product of the marginal distributions. We pair this approach with effi...

Analysis of independent components in biomedical signals