Stability and Chaos of a Class of Learning Algorithms for ICA Neural Networks
Neural Processing Letters
DIAR: Advances in Degradation Modeling and Processing
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
RSLDI: Restoration of single-sided low-quality document images
Pattern Recognition
IEEE Transactions on Signal Processing
Fast kernel-based independent component analysis
IEEE Transactions on Signal Processing
On the convergence of ICA algorithms with symmetric orthogonalization
IEEE Transactions on Signal Processing
IEEE Transactions on Neural Networks
Blind instantaneous noisy mixture separation with best interference-plus-noise rejection
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Learning Topographic Representations of Nature Images with Pairwise Cumulant
Neural Processing Letters
Consistency and asymptotic normality of FastICA and bootstrap FastICA
Signal Processing
Distributional convergence of subspace estimates in FastICA: a bootstrap study
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
On the convergence of ICA algorithms with weighted orthogonal constraint
Digital Signal Processing
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The fast independent component analysis (FastICA) algorithm is one of the most popular methods to solve problems in ICA and blind source separation. It has been shown experimentally that it outperforms most of the commonly used ICA algorithms in convergence speed. A rigorous local convergence analysis has been presented only for the so-called one-unit case, in which just one of the rows of the separating matrix is considered. However, in the FastICA algorithm, there is also an explicit normalization step, and it may be questioned whether the extra rotation caused by the normalization will affect the convergence speed. The purpose of this paper is to show that this is not the case and the good convergence properties of the one-unit case are also shared by the full algorithm with symmetrical normalization. A local convergence analysis is given for the general case, and the global behavior is illustrated numerically for two sources and two mixtures in several typical cases