Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Fully Complex Multi-Layer Perceptron Network for Nonlinear Signal Processing
Journal of VLSI Signal Processing Systems
Improving the Performance of Infomax Using Statistical Signal Processing Techniques
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Approximation by fully complex multilayer perceptrons
Neural Computation
Complex independent component analysis of frequency-domain electroencephalographic data
Neural Networks - Special issue: Neuroinformatics
Independent Component Analysis Applied to fMRI Data: A Generative Model for Validating Results
Journal of VLSI Signal Processing Systems
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
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Independent component analysis (ICA) for separating complex-valued sources is needed for convolutive source-separation in the frequency domain, or for performing source separation on complex-valued data, such as functional magnetic resonance imaging or radar data. Previous complex Infomax approaches that use nonlinear functions in the updates have proposed using bounded (and hence non-analytic) nonlinearities. In this paper, we propose using an analytic (and hence unbounded) complex nonlinearity for Infomax for processing complex-valued sources. We show by simulation examples that using an analytic nonlinearity for processing complex data has a number of advantages. First, when compared to split-complex approaches (i.e., approaches that split the real and imaginary data into separate channels), the shape of the performance surface is improved resulting in better convergence characteristics. We also show that using an analytic complex-valued function for the nonlinearity is more effective in generating the higher order statistics required to establish independence when compared to complex nonlinear functions, i.e., functions that are 驴 驴 驴