Elements of information theory
Elements of information theory
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Complexity Pursuit: Separating Interesting Components from Time Series
Neural Computation
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Letters: A fast fixed-point algorithm for complexity pursuit
Neurocomputing
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
Detection and separation in space time block coding using noisy compound PCA - ICA model
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
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 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, we consider the estimation of the data model of ICA when Gaussian noise is present and the independent components are time dependent. We derive a simple algorithm combining Gaussian moments and complexity pursuit for noisy ICA. Validity and performance of the described approaches are demonstrated by computer simulations.