Topographic Independent Component Analysis

  • Authors:

  • Aapo Hyvärinen;Patrik O. Hoyer;Mika O. Inki

  • Affiliations:

  • Neural Networks Research Centre, Helsinki University of Technology, FIN-02015 HUT, Finland;Neural Networks Research Centre, Helsinki University of Technology, FIN-02015 HUT, Finland;Neural Networks Research Centre, Helsinki University of Technology, FIN-02015 HUT, Finland

  • Venue:
  • Neural Computation
  • Year:
  • 2001

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Abstract

In ordinary independent component analysis, the components are assumed to be completely independent, and they do not necessarily have any meaningful order relationships. In practice, however, the estimated "independent" components are often not at all independent. We propose that this residual dependence structure could be used to define a topographic order for the components. In particular, a distance between two components could be defined using their higher-order correlations, and this distance could be used to create a topographic representation. Thus, we obtain a linear decomposition into approximately independent components, where the dependence of two components is approximated by the proximity of the components in the topographic representation.