Linear Discriminant Analysis Code

this function converts data from its original space to LDA space.

% [sLDA WLDA M WPCA]=mylda(data,class,n)
% this function written by muhammet balcilar
% yildiz technical university computer engineering department
% istanbul turkiye 2011

% this function convert data from its original space to LDA space
% if number of data samples is less than number of diamension, PCA is
% implemented for reducing number of diamension to #samples-1.
% after PCA, LDA is implemented for reducing diamention to n.

% data is consist of M rows(sample size), N cols(dimensions)
% class is consist of M rows(sample size), 1 cols , each element of class
% is shows class number of each data sample
% (class number must be integer 1 to classsize)
% n is the number of outputs data diamensions.(optionally)
% sLDA is consist of M rows(sample size) n cols(new dimensions)
% WPCA is translate matrix which convert to original space to PCA space
% M is the mean vector of training set
% WLDA is the translate matrix which convert to original space to LDA space
% exaple: there are 4 samples which have 5 diamensions.first two samples
% are member of class 1 others are member of class 2.
% Train= [5.6,5.7,5.5,5.7 5.6;
% 5.7,5.3,5.1,5.0 5.2;
% 10.6,9.9,10.4,10.7 10.2;
% 10.7,9.8,9.9,10 10];
% Class=[1;1;2;2];
% [sLDA WLDA M WPCA]=mylda(Train,Class)
% Test= [4.9 5.5 4.8 5.7 5];
% LDATEST = (Test-M)*WPCA*WLDA