Fast kernel-based independent component analysis
IEEE Transactions on Signal Processing
Blind underdetermined mixture identification by joint canonical decomposition of HO cumulants
IEEE Transactions on Signal Processing
Fast kernel density independent component analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Markovian blind image separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Compression of multicomponent satellite images using independent components analysis
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind partial separation of instantaneous mixtures of sources
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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This paper provides fast algorithms to perform independent component analysis based on the mutual information criterion. The main ingredient is the binning technique and the use of cardinal splines, which allows the fast computation of the density estimator over a regular grid. Using a discretized form of the entropy, the criterion can be evaluated quickly together with its gradient, which can be expressed in terms of the score functions. Both offline and online separation algorithms have been developed. Our density, entropy, and score estimators also have their own interest.