Nonlinear PCA type approaches for source separation and independent component analysis

Studies the application of some nonlinear neural pricipal component analysis (PCA) type approaches to the separation of independent source signals from their linear mixture. This problem is important in signal processing and communications, and it cannot be solved using standard PCA. Using prewhitening and appropriate choice of nonlinearities, several algorithms proposed by the authors yield good separation results for sub-Gaussian (or super-Gaussian) source signals. The authors discuss the related problem of estimating the basis vectors in independent component analysis briefly, too.

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