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', to appear in Computational Statistics and Data Analysis in 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', to appear in Computational Statistics and Data Analysis in
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. Schl�l, 22.Aug.2006
$Id: train_lda_sparse.m,v 1.6 2007/01/24 15:59:03 schloegl Exp $
This is part of the BIOSIG-toolbox http://biosig.sf.net/