Linear Discriminant Analysis for the Small Sample Size Problem as described in
 Algorithm 1 of J. Duintjer Tebbens, P. Schlesinger: 'Improving
 Implementation of Linear Discriminant Analysis for the High Dimension/Small Sample Size
 Problem', Computational Statistics and Data Analysis, vol. 52, no. 1, pp. 423-437, 2007.  
 Input:
               X                 ......       (sparse) training data matrix
               G                 ......       group coding matrix of the training data
               test              ......       (sparse) test data matrix
               Gtest             ......       group coding matrix of the test data
               par               ......       if par = 0 then classification exploits sparsity too
               tol               ......       tolerance to distinguish zero eigenvalues
 Output:
               err               ......       Wrong classification rate (in %)
               trafo             ......       LDA transformation vectors

 Reference(s): 
 J. Duintjer Tebbens, P. Schlesinger: 'Improving
 Implementation of Linear Discriminant Analysis for the High Dimension/Small Sample Size
 Problem', Computational Statistics and Data Analysis, vol. 52, no. 1, 
 pp. 423-437, 2007.

 Copyright (C) by J. Duintjer Tebbens, Institute of Computer Science of the Academy of Sciences of the Czech Republic,
 Pod Vodarenskou vezi 2, 182 07 Praha 8 Liben, 18.July.2006. 
 This work was supported by the Program Information Society under project
 1ET400300415.


 Modified for the use with Matlab6.5 by A. Schloegl, 22.Aug.2006

	$Id$
       This function is part of the NaN-toolbox
       http://pub.ist.ac.at/~schloegl/matlab/NaN/

Package: nan