pca_new_final.m

matlab图像处理工具相

function [x,vec]=pca_new_final(data)
% Remember that each column of the data matrix(input matrix) represent one image or pattern  


[r,c]=size(data);
% Compute the mean of the data matrix "The mean of each row"
m=mean(data')';
% Subtract the mean from each image 
d=data-repmat(m,1,c);



% Compute the covariance matrix (co)
co=d*d';

% Compute the eigen values and eigen vectors of the covariance matrix
[eigvector,eigvl]=eig(co);


% Sort the eigen vectors according to the eigen values
eigvalue = diag(eigvl);
[junk, index] = sort(-eigvalue);
eigvalue = eigvalue(index);
eigvector = eigvector(:, index);

% Compute the number of eigen values that greater than zero
count1=0;
for i=1:size(eigvalue,1)
    if(eigvalue(i)>0)
        count1=count1+1;
    end
end

% We can use all the eigen vectors but this method will increase the
% computation time and complixity
%vec=eigvector(:,:);

% And also we can use the eigen vectors that the corresponding eigen values is greater than zero and this method will decrease the
% computation time and complixity

vec=eigvector(:,1:count1);

% Compute the feature matrix (the space that will use it to project the testing image on it)
x=vec'*d;