High-order contrasts for independent component analysis
Neural Computation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An Adaptive Method for Subband Decomposition ICA
Neural Computation
Separation of statistically dependent sources using an L2-distance non-Gaussianity measure
Signal Processing - Special section: Distributed source coding
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
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The purpose of this paper is to develop novel Blind Source Separation (BSS) algorithms from linear mixtures of them, which enable to separate dependent source signals. Most of the proposed algorithms for solving BSS problem rely on independence or at least uncorrelation assumption of the source signals. Here, we show that maximization of the nonGaussianity(NG) measure can separate the statistically dependent source signals and the novel NG measure is given by the Hall Euclidean distance. The proposed separation algorithm can result in the famous FastICA algorithm. Simulation results show that the proposed separation algorithm is able to separate the dependent signals and yield ideal performance.