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brede_ica_nbs_est

PURPOSE ^

brede_ica_nbs_est - Non-symmetric Bell & Sejnowski ICA

SYNOPSIS ^

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

DESCRIPTION ^

 brede_ica_nbs_est    - Non-symmetric Bell & Sejnowski ICA

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

       Input:    X    Data matrix, objects x sensors

       Property: Components [ {20} | positive integer ] 
                 Winit      [ {eye} | rand | randn | a matrix with
                            the size of A ] Initialization of the
                            inverse mixing matrix (the demixing
                            matrix) 

       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 brede_opt_cg via the brede_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] = brede_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 BREDE, BREDE_ICA_BS_EST, BREDE_SVD_EST, BREDE_OPT_CG.

 $Id: brede_ica_nbs_est.m,v 1.5 2004/09/22 15:04:53 fnielsen Exp $

CROSS-REFERENCE INFORMATION ^

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