pca2.m

%% PCA version2
% Input: X, y, k
% Output: W, mu
function [W , mu ] = pca2 (X , y , k)
[n , d] = size (X );
mu = mean (X);
Xm = X - repmat (mu , size(X,1) , 1) ;
if(n >d )
    C = Xm'* Xm ;
    [W , D] = eig (C) ;
    % sort eigenvalues and eigenvectors
    [D , i] = sort ( diag (D) , 'descend');
    W = W (: , i );
    % keep k components
    W = W (: ,1: k);
else
    C = Xm *Xm';
    %C = cov(Xm ’);
    [W , D] = eig (C) ;
    % multiply with data matrix
    W = Xm'* W ;
    % normalize eigenvectors
    for i =1: n
        W (: , i) = W (: , i )/ norm (W (: , i)) ;
    end
    % sort eigenvalues and eigenvectors
    [D , i] = sort ( diag (D) , 'descend');
    W = W (: , i );
    % keep k components
    W = W (: ,1: k);
end
end