Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive blind separation of independent sources: a deflation approach
Signal Processing
Matrix computations (3rd ed.)
Independent component analysis: algorithms and applications
Neural Networks
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
IEEE Transactions on Signal Processing
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Neural Networks
Approach and applications of constrained ICA
IEEE Transactions on Neural Networks
Fast independent component analysis using a new property
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Independent component analysis based on first-order statistics
Signal Processing
Jacobi iterations for Canonical Dependence Analysis
Signal Processing
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This paper presents a method for signal extraction based on conditional second-order moments of the output of the extraction filter. The estimator of the filter is derived from an approximate maximum likelihood criterion conditioned on a presence indicator of the source of interest. The conditional moment is shown to be a contrast function under the conditions that 1) all cross-moments of the same order between the source signal of interest and the other source signals are null and 2) that the source of interest has the largest conditional moment among all sources. for the two-source two-observation case, this allows us to derive the theoretical recovery bounds of the contrast when the conditional cross-moment does not vanish. A comparison with empirical results confirms these bounds. Simulations show that the estimator is quite robust to additive Gaussian distributed noise. Also through simulations, we show that the error level induced by a rough approximation of the presence indicator shows a strong similarity with that of additive noise. The robustness, with respect both to noise and to inaccuracies in the prior information about the source presence, guarantees a wide applicability of the proposed method.