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lyngby_ica_nbs_est

(export/lyngby/lyngby_ica_nbs_est.m)


Function Synopsis

[S, A] = lyngby_ica_nbs_est(X, varargin)

Help text

 lyngby_ica_nbs_est    - Non-symmetric Bell & Sejnowski ICA

       function [S, A] = lyngby_ica_nbs_est(X, varargin)

       Input:    X    Data matrix, objects x sensors

       Property: Components [ {20} | positive integer ] 

       Output:   S    Source matrix, objects x sources
                 A    Mixing matrix, sources x sensors

       Independent component analysis with non-symmetric Bell and
       Sejnowski, ie, non-square mixing  matrix. Optimization of the
       mixing matrix is done by conjugate gradient optimization. The
       estimated mixing matrix is square and is applied on the right
       side of the source matrix:  

                          X = S * A

       An initial singular value decomposition is performed and the
       ordinary ICA algorithm is performed in the subspace.
       The optimization is performed with conjugate gradient
       implemented in lyngby_opt_cg via the lyngby_ica_bs_est
       function. 

       The property 'Components' governs how many sources are
       found. It will be the minimum of the rank of X and the number
       given by 'Components'.

       Example:
         Strue = randn(500, 2).^3;
         Atrue = [ 3 4 -8 ; 1 4 2 ];
         X = Strue * Atrue;
         [S, A] = lyngby_ica_nbs_est(X, 'components', 2);
         figure, plot(X(:,1), X(:,2), '.', ...
               5*[-A(1,1) A(1,1)], 5*[-A(1,2) A(1,2)], 'r-', ...
               5*[-A(2,1) A(2,1)], 5*[-A(2,2) A(2,2)], 'g-')
 
       See also LYNGBY, LYNGBY_ICA_BS_EST, LYNGBY_ICA_MS_EST,
                LYNGBY_SVD, LYNGBY_OPT_CG. 

 $Id: lyngby_ica_nbs_est.m,v 1.1 2003/02/20 17:43:34 fnielsen Exp $

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