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Function Synopsis

[f, df] = cost(W, X)

Help text

 lyngby_ica_bs_est     - Bell & Sejnowski ICA estimation

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

       Input:  X    Matrix with data, objects x sensors

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

       Independent component analysis as Bell and
       Sejnowski. 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
                          X = S * A

       This function is based on the icaML.m function in the 
       ICA:DTU Toolbox by Thomas Kolenda.

         Strue = randn(500, 2).^3;
         Atrue = [ 3 4 ; 1 4 ];
         X = Strue * Atrue;
         [S, A] = lyngby_ica_bs_est(X);
         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-')
       Ref: Kolenda T, Winther O, LK Hansen, ICA:DTU Toolbox,

       See also LYNGBY, LYNGBY_SVD.

 $Id: lyngby_ica_bs_est.m,v 1.1 2003/02/20 16:28:46 fnielsen Exp $

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