An Efficient Pairwise Kurtosis Optimization Algorithm for Independent Component Analysis

  • Fei Ge
  • Jinwen Ma
Conference paper

DOI: 10.1007/978-3-642-14831-6_13

Part of the Communications in Computer and Information Science book series (CCIS, volume 93)
Cite this paper as:
Ge F., Ma J. (2010) An Efficient Pairwise Kurtosis Optimization Algorithm for Independent Component Analysis. In: Huang DS., McGinnity M., Heutte L., Zhang XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg

Abstract

In the framework of Independent Component Analysis (ICA), kurtosis has been used widely in designing source separation algorithms. In fact, the sum of absolute kurtosis values of all the output components is an effective objective function for separating arbitrary sources. In this paper, we propose an efficient ICA algorithm via a modified Jacobi optimization procedure on the kurtosis-sum objective function. The optimal rotation angle for any pair of the output components can be solved directly. It is demonstrated by numerical simulation experiments that our proposed algorithm can be even more computationally efficient than the FastICA algorithm under the same separation performance.

Keywords

Independent Component Analysis kurtosis pairwise optimization Jacobi algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fei Ge
    • 1
  • Jinwen Ma
    • 1
  1. 1.Department of Information Science, School of Mathematical Sciences and LMAMPeking UniversityBeijingChina

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