Titlebook: Advances in Independent Component Analysis; Mark Girolami Book 2000 Springer-Verlag London 2000 Ensembl.artificial intelligence.artificial

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R. B. Dunn,J. B. Zirker,J. M. Beckers9]. Such an approach connects classical EM estimation to the ICA framework. Unfortunately, computational problems arise when the class densities, that underly the observed data are degenerate or are poorly conditioned. This appears to be very likely in many applications. In this chapter we approach

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R. B. Dunn,J. B. Zirker,J. M. Beckersal imaging data such as that obtained by functional magnetic resonance imaging functional magnetic resonance imaging (fMRI) and optical imaging optical imaging (OI) of brain activity. These techniques were developed in order to help address some current issues involving the nature of the haemodynami

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Spicules and Their SurroundingsReconstruction of statistically independent source signals from linear mixtures is relevant to many signal processing contexts [1,3,6,11,22]. Considered a generalization of principal component analysis, the problem is often referred to as independent component analysis (ICA) [9].

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Properties of the Solar Filigree StructureMultichannel recordings of the electromagnetic fields emerging from neural currents in the brain generate large amounts of data. Suitable feature extraction methods are, therefore, useful to facilitate the representation and interpretation of the data.

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R. B. Dunn,J. B. Zirker,J. M. BeckersThe basic problem of ICA is defined for the noiseless case, where the sources and observations have the following linear relation, . = . (11.1) . ∈ .., . ∈ .., . ∈ ..

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