Robust Independent Component Analysis Based on Two Scatter Matrices


  • Klaus Nordhausen University of Tampere, Finland
  • Hannu Oja University of Tampere, Finland
  • Esa Ollila Helsinki University of Technology, Finland



Oja, Sirkiä, and Eriksson (2006) and Ollila, Oja, and Koivunen (2007) showed that, under general assumptions, any two scatter matrices with the so called independent components property can be used to estimate the unmixing matrix for the independent component analysis (ICA). The method is a generalization of Cardoso’s (Cardoso, 1989) FOBI estimate which uses the regular covariance matrix and a scatter matrix based on fourth moments. Different choices of the two scatter matrices are compared in a simulation study. Based on the study, we recommend always the use of two robust scatter matrices. For possible asymmetric independent components, symmetrized versions of the scatter matrix estimates should be used.


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How to Cite

Nordhausen, K., Oja, H., & Ollila, E. (2016). Robust Independent Component Analysis Based on Two Scatter Matrices. Austrian Journal of Statistics, 37(1), 91–100.