A new ICA algorithm for blind source separation

We propose a nonparametric independent component analysis (ICA) algorithm for the case of instantaneous and linear mixtures. Our algorithm combines minimization of correlation among nonlinear expansions of the output signals with good initialization derived from a search guided by statistical tests for independence. Simulation results obtained from both synthetic and real-life data show that our method provides consistent results and compares favorably to existing ICA algorithms.