新建文件夹.zip_contain6g1_pca_varietysfk

% prophet mohammed said [ALLAH will help any one helped his/her brother/sister] PBUH %This code to apply PCA (Principal Component Analysis) % for any information please send to engalaatharwat@hotmail.com %Egypt - HICIT - +20106091638 % Remember that each column of the data matrix(input matrix) represent one image or pattern % Note: the data here represent two classes % Class 1: data(:,1:4) % Class 2: data(:,5:8) data = [1 1 2 0 7 6 7 8 3 2 3 3 4 5 5 4]; %data1=xlsread('C:\Users\sxw\Desktop\论文2\算法\仿真数据\信号特征.xls') %data=data1(:,2)'; [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 [Centering the data] 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 (you can select any threshold) 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(Threshold) 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; % If you have test data do the following t=[1;1] % this test data is close to the first class %Subtract the mean from the test data t=t-m; %Project the testing data on the space of the training data t=vec'*t; % Then if you want to know what is the class of this test data? just use % any classifier (In our case we used minimum distance classifier) alldata=t'; alldata(2:size(x,2)+1,:)=x' dist=pdist(alldata); [a,b]=min(dist(:,1:size(x,2))) % So, b determine the closest observation to test data;