Stochastic ICA Contrast Maximisation Using Oja's Nonlinear PCA Algorithm
Independent Component Analysis (ICA) is an important extension of linear Principal Component Analysis (PCA). PCA performs a data transformation to provide independence to second order, that is, decorrelation. ICA transforms data to provide approximate independence up to and beyond second order yielding transformed data with fully factorable probability densities. The linear ICA transformation has been applied to the classical statistical signal-processing problem of Blind Separation of Sources (BSS), that is, separating unknown original source signals from a mixture whose mode of mixing is undetermined. In this paper it is shown that Oja's Nonlinear PCA algorithm performs a general stochastic online adaptive ICA. This analysis is corroborated with three simulations. The first separates unknown mixtures of original natural images, which have sub-Gaussian densities, the second separates linear mixtures of natural speech whose densities are super-Gaussian. Finally unknown mixtures of original images, which have both sub- and super-Gaussian densities are separated.