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Fisher Information in Source Separation Problems

  • Vincent Vigneron
  • Christian Jutten
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3195)

Abstract

The ability to estimate a specific set of parameters, without regard to an unknown set of other parameters that influence the measured data, or nuisance parameters, is described by the Fisher Information matrix (FIM), and its inverse the Cramer-Rao bound. In many adaptive gradient algorithm, the effect of multiplication by the latter is to make the update larger in directions in which the variations of the parameter θ have less statistical significance. In this paper, we examine the relationship between the Fisher information and the covariance of the estimation error under the scope of the source separation problem.

Keywords

Independent Component Analysis Maximum Likelihood Estimator Fisher Information Independent Component Analysis Nuisance Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Vincent Vigneron
    • 1
  • Christian Jutten
    • 1
  1. 1.INPG-LIS CNRS UMR 5083Grenoble cedexFrance

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