• DocumentCode
    2400438
  • Title

    One-unit contrast functions for independent component analysis: a statistical analysis

  • Author

    Hyvarinen, Aapo

  • Author_Institution
    Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Espoo
  • fYear
    1997
  • fDate
    24-26 Sep 1997
  • Firstpage
    388
  • Lastpage
    397
  • Abstract
    The author (1997) introduced a large family of one-unit contrast functions to be used in independent component analysis (ICA). In this paper, the family is analyzed mathematically in the case of a finite sample. Two aspects of the estimators obtained using such contrast functions are considered: asymptotic variance, and robustness against outliers. An expression for the contrast function that minimizes the asymptotic variance is obtained as a function of the probability densities of the independent components. Combined with robustness considerations, these results provide strong arguments in favor of the use of contrast functions based on slowly growing functions, and against the use of kurtosis, which is the classical contrast function
  • Keywords
    matrix algebra; probability; signal processing; statistical analysis; asymptotic variance; independent component analysis; one-unit contrast functions; outliers; probability densities; robustness; statistical analysis; Blind source separation; Covariance matrix; Gaussian noise; Independent component analysis; Information science; Probability; Robustness; Signal processing; Statistical analysis; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
  • Conference_Location
    Amelia Island, FL
  • ISSN
    1089-3555
  • Print_ISBN
    0-7803-4256-9
  • Type

    conf

  • DOI
    10.1109/NNSP.1997.622420
  • Filename
    622420