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    Redundant Data Elimination in Independent Component Analysis

    Access Status
    Fulltext not available
    Authors
    Liu, Xianhua
    Randall, R.
    Date
    2005
    Type
    Conference Paper
    
    Metadata
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    Citation
    Liu, Xianhua and Randall, R.B. 2005. Redundant Data Elimination in Independent Component Analysis, in Bouzerdoum, A. and Beghdadi, A. (ed), 8th International Symposium on Signal Processing and its Applications, Aug 28-31 2005, pp. 29-32. Sydney, Australia: Institute of Electrical and Electronics Engineers.
    Source Title
    Proceedings of the 8th International Symposium on Signal Processing and its Applications
    Source Conference
    8th International Symposium on Signal Processing and its Applications
    DOI
    10.1109/ISSPA.2005.1580188
    ISBN
    0-7803-9243-4
    URI
    http://hdl.handle.net/20.500.11937/26761
    Collection
    • Curtin Research Publications
    Abstract

    Independent component analysis involves a lot of data in statistical calculations. This paper studies the model by examining which part of the data is essential and which part is redundant for defining the mixing system and proposes an idea called redundant data elimination. Statistical properties change in the direction of uniform distribution as redundant data are eliminated, yet the model still holds and the solution still exists. A theoretical explanation is given of the geometrical transformation of independent sources. The above reasoning is verified by separation experiment. It is shown that this idea can also improve model match for unsymmetrical sources.

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