Rapid and Brief Communication: Subspace independent component analysis using vector kurtosis

Authors:
Alok Sharma;Kuldip K. Paliwal
Affiliations:
Signal Processing Laboratory, Griffith University, Brisbane, Australia;Signal Processing Laboratory, Griffith University, Brisbane, Australia
Venue:
Pattern Recognition
Year:
2006

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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.