论文信息 - Nonlinearity and Separation Capability: Further Justiication for the Ica Algorithm with a Learned Mixture of Parametric Densities

Nonlinearity and Separation Capability: Further Justiication for the Ica Algorithm with a Learned Mixture of Parametric Densities

We discuss the relation between nonlinearity and separation capability in the information-theoretic ICA scheme. We propose with justiication that a `loose matching' between the nonlinearity and source distribution is needed. These results give further support to the implementation technique by a learned mixture of parametric densities.

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