• DocumentCode
    396766
  • Title

    SOMICA - an application of self-organizing maps to geometric independent component analysis

  • Author

    Theis, Fabian J. ; Puntonet, Carlos G. ; Lang, Elmar W.

  • Author_Institution
    Inst. of Biophys., Regensburg Univ., Germany
  • Volume
    2
  • fYear
    2003
  • fDate
    20-24 July 2003
  • Firstpage
    1318
  • Abstract
    Guided by the principles of geometric independent component analysis (ICA), we present a new approach (SOM-ICA) to linear geometric ICA using self-organizing map (SOM). We observe a considerable improvement in separation quality of different distributions, albeit at high computational costs. The SOMICA algorithm is therefore primarily interesting from a theoretical point of view bringing together ICA and SOMs; this intersection could lead to new proofs in geometric ICA based on similar theorems in the SOM theory.
  • Keywords
    blind source separation; independent component analysis; self-organising feature maps; SOMICA algorithm; blind source separation; geometric independent component analysis; linear geometric ICA; self-organizing map; Biophysics; Blind source separation; Computational efficiency; Covariance matrix; Independent component analysis; Self organizing feature maps; Source separation; Statistics; Symmetric matrices; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2003. Proceedings of the International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7898-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2003.1223886
  • Filename
    1223886