Subspace independent component analysis using vector kurtosis

descriptionPublicationkeyboard_double_arrow_right Article 01 Nov 2006 English Publisher:Elsevier BVJournal:Pattern Recognition, volume 39, pages 2,227-2,232 (issn: 0031-3203,

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Authors: Alok Sharma; Kuldip K. Paliwal;

doi: 10.1016/j.patcog.2006.04.021

handle: 10072/14346

Subspace independent component analysis using vector kurtosis

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Abstract

This discussion presents a new perspective of subspace independent component analysis (ICA). The notion of a function of cumulants (kurtosis) is generalized to vector kurtosis. This vector kurtosis is utilized in the subspace ICA algorithm to estimate subspace independent components. One of the main advantages of the presented approach is its computational simplicity. The experiments have shown promising results in estimating subspace independent components. ; Griffith Sciences, Griffith School of Engineering ; No Full Text

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Griffith University
Australia

Keywords

Information systems

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