Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A neural net for blind separation of nonstationary signals
Neural Networks
A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis by general nonlinear Hebbian-like learning rules
Signal Processing - Special issue on neural networks
Optimization by Vector Space Methods
Optimization by Vector Space Methods
Complexity Pursuit: Separating Interesting Components from Time Series
Neural Computation
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Letters: Nonlinear innovation to blind source separation
Neurocomputing
Nonlinear Innovation to Noisy Blind Source Separation Based on Gaussian Moments
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
A fixed-point algorithm for blind source separation with nonlinear autocorrelation
Journal of Computational and Applied Mathematics
Blind source separation with nonlinear autocorrelation and non-Gaussianity
Journal of Computational and Applied Mathematics
Fast nonlinear autocorrelation algorithm for source separation
Pattern Recognition
Letters: Gaussian moments for noisy complexity pursuit
Neurocomputing
Blind Source Separation Using Quadratic form Innovation
Neural Processing Letters
Hybrid linear and nonlinear complexity pursuit for blind source separation
Journal of Computational and Applied Mathematics
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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.