Semi-nonnegative Independent Component Analysis: The (3,4)-SENICAexp Method

  • Julie Coloigner
  • Laurent Albera
  • Ahmad Karfoul
  • Amar Kachenoura
  • Pierre Comon
  • Lotfi Senhadji
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6365)

Abstract

To solve the Independent Component Analysis (ICA) problem under the constraint of nonnegative mixture, we propose an iterative algorithm, called (3,4)-SENICAexp. This method profits from some interesting properties enjoyed by third and fourth order statistics in the presence of mixed independent processes, imposing the nonnegativity of the mixture by means of an exponential change of variable. This process allows us to obtain an unconstrained problem, optimized using an ELSALS-like procedure. Our approach is tested on synthetic magnetic resonance spectroscopic imaging data and compared to two existing ICA methods, namely SOBI and CoM2.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Julie Coloigner
    • 1
    • 2
  • Laurent Albera
    • 1
    • 2
  • Ahmad Karfoul
    • 5
  • Amar Kachenoura
    • 1
    • 2
  • Pierre Comon
    • 3
    • 4
  • Lotfi Senhadji
    • 1
    • 2
  1. 1.Inserm, UMR 642RennesFrance
  2. 2.Université de Rennes 1, LTSIRennesFrance
  3. 3.CNRS, UMR 6070Sophia AntipolisFrance
  4. 4.I3S, Université de Nice Sophia AntipolisFrance
  5. 5.Faculty of Mechanical and Electrical EngineeringUniversity Al-BaathHomsSyria

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