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Least-dependent-component analysis based on mutual information Harald Stgbauer, Alexander Kraskov, Sergey A. Astakhov, and Peter Grassberger
 

Summary: Least-dependent-component analysis based on mutual information
Harald Stögbauer, Alexander Kraskov, Sergey A. Astakhov, and Peter Grassberger
John-von-Neumann Institute for Computing, Forschungszentrum Jülich, D-52425 Jülich, Germany
(Received 11 May 2004; published 13 December 2004)
We propose to use precise estimators of mutual information (MI) to find the least dependent components in
a linearly mixed signal. On the one hand, this seems to lead to better blind source separation than with any
other presently available algorithm. On the other hand, it has the advantage, compared to other implementa-
tions of "independent" component analysis (ICA), some of which are based on crude approximations for MI,
that the numerical values of the MI can be used for (i) estimating residual dependencies between the output
components; (ii) estimating the reliability of the output by comparing the pairwise MIs with those of remixed
components; and (iii) clustering the output according to the residual interdependencies. For the MI estimator,
we use a recently proposed k-nearest-neighbor-based algorithm. For time sequences, we combine this with
delay embedding, in order to take into account nontrivial time correlations. After several tests with artificial
data, we apply the resulting MILCA (mutual-information-based least dependent component analysis) algorithm
to a real-world dataset, the ECG of a pregnant woman.
DOI: 10.1103/PhysRevE.70.066123 PACS number(s): 02.50.Sk, 89.70. c, 87.19.Hh, 05.45.Tp
I. INTRODUCTION
"Independent" component analysis (ICA) is a statistical
method for transforming an observed multicomponent data
set x t =(x1 t ,x2 t , ... ,xn t ) into components that are sta-

  

Source: Astakhov, Sergey - John von Neumann Institute for Computing, Forschungszentrum Jülich

 

Collections: Computer Technologies and Information Sciences; Physics