Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation
Neural Computation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
General approach to blind source separation
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixture of sources based on orderstatistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Maximum likelihood parameter and rank estimation in reduced-rankmultivariate linear regressions
IEEE Transactions on Signal Processing
Neural networks for blind decorrelation of signals
IEEE Transactions on Signal Processing
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
Convergence analysis of a discrete-time single-unit gradient ICA algorithm
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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Blind source separation (BSS) is an emerging research field in both theory and applications. In this paper we propose a kurtosis maximization algorithm-Sequential Extraction Algorithm, which can extract the source signals sequentially. This approach is based on an algorithm for separating one signal (Algorithm 1) and some technique to eliminate the accumulating errors, which often occur in the sequential extraction steps. In Algorithm 1, a new criterion to judge whether the separated signal is an original source signal, is proposed. In Sequential Extraction Algorithm, we propose a new approach to eliminate accumulating errors, which is caused in the sequential extraction process. This approach is based on the cost function involved in this algorithm, and thus, is different from those available in literature.