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