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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 $

### Cross-Reference Information

This function calls

Produced by mat2html on Wed Jul 29 15:43:40 2009

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