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ICA and ICAMM Methods

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On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling

Part of the book series: Springer Theses ((Springer Theses,volume 4))

Abstract

Independent component analysis (ICA) aims to separate hidden sources from their observed linear mixtures without any prior knowledge. The only assumption about the sources is that they are mutually independent. Thus, the goal is blind source estimation; although it has been recently alleviated by incorporating prior knowledge about the sources into the ICA model in the so-called semi-blind source separation. This technique has been widely used in many fields of application such as telecommunications, bioengineering, and material testing.

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Authors and Affiliations

  1. Department of Communications, School of Telecommunication Engineering, Polytechnic University of Valencia, Camino de Vera s/n, 46022, Valencia, Spain

    Addisson Salazar

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  1. Addisson Salazar
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Salazar, A. (2013). ICA and ICAMM Methods. In: On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling. Springer Theses, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30752-2_2

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  • DOI: https://doi.org/10.1007/978-3-642-30752-2_2

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