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brede_ica_bs_est

PURPOSE ^

brede_ica_bs_est - Bell & Sejnowski ICA estimation

SYNOPSIS ^

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

DESCRIPTION ^

 brede_ica_bs_est     - Bell & Sejnowski ICA estimation

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

       Input:    X     Matrix with data, objects x sensors

       Property: 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 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
       matrix: 
                          X = S * A

       The sources are scaled to have a variance of one and the sign
       is determined so the skewnessess of the sources are positive. 

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

       Example:
         Strue = randn(500, 2).^3;
         Atrue = [ 3 4 ; 1 4 ];
         X = Strue * Atrue;
         [S, A] = brede_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,
               http://isp.imm.dtu.dk/toolbox/ 

       See also BREDE, BREDE_ICA_NBS_EST, BREDE_MAT_ICA,
                BREDE_SVD_EST, BREDE_NMF_EST.

 $Id: brede_ica_bs_est.m,v 1.9 2004/09/22 15:37:58 fnielsen Exp $

CROSS-REFERENCE INFORMATION ^

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