Energy predictability to blind source separation

A blind source separation (BSS) method based on the energy (square) predictability of original sources is proposed. The method exploits the nonstationatity of sources in the sense that the variance of each source signal can be assumed to change smoothly against time. In contrast to linear predictability, it is shown that nonlinear predictability can also be used for BSS. Simulations verify the efficient implementation of the proposed method, especially its robustness to the outliers

A fast fixed-point algorithm for complexity pursuit

Complexity pursuit is a recently developed algorithm using the gradient descent for separating interesting components from time series. It is an extension of projection pursuit to time series data and the method is closely related to blind separation of time-dependent source signals and independent component analysis (ICA). In this paper, a fixed-point algorithm for complexity pursuit is introduced. The fixed-point algorithm inherits the advantages of the well-known FastICA algorithm in ICA, which is very simple, converges fast, and does not need choose any learning step sizes. (c) 2005 Elsevier B.V. All rights reserved