pca人脸识别.rar_awareocl_matlab PCA 人脸_pca orl_人脸识别

load imagedata % load imagenewdata classnum=40; everypersonnums=5; pcamaxnums=60;%设定pca最大的前maxnums特征值 ldamaxnums=20;%lda操作选取的特征数 % pstudy=newpstudy; % ptest=newptest; %每幅训练图象减去平均值 [mstudy,nstudy]=size(pstudy); pstudymean=mean(pstudy(:,1:nstudy-1)); pstudy1=pstudy(:,1:nstudy-1)-pstudymean(ones(mstudy,1),:); %每幅测试图象减去平均值 [mtest,ntest]=size(ptest); ptestmean=mean(ptest(:,1:ntest-1)); ptest1=ptest(:,1:ntest-1)-ptestmean(ones(mtest,1),:); %计算训练样本协方差矩阵及其特征值，特征向量 f=(1/sqrt(mstudy))*pstudy1; L=f*f'; [eignvectors,eignvalues] = eig(L); %将特征值从大到小排列 % [ee,kk]=sort(diag(eignvalues),'descend'); [ee,kk]=sort(diag(eignvalues)); kk=flipud(kk); %取得排列后特征值对应的特征向量 eigns=eignvectors(:,kk); %求投影矩阵 U=f'*eigns; %把矩阵单位化 for i=1:mstudy U(:,i)=U(:,i)/norm(U(:,i)); end %选maxnums个特征向量组成子空间 UU(:,1:pcamaxnums)=U(:,1:pcamaxnums); %求pca在训练集上的投影矩阵 pcastudy2=pstudy1*UU; %计算类间散布矩阵SB pcastudy2mean=mean(pcastudy2);%求在测试集上总体平均值 for i=1:classnum k1=(i-1)*everypersonnums+1; k2=(i-1)*everypersonnums+5; everypersonmean(i,:)=sum(pcastudy2(k1:k2,:))/everypersonnums; end SB=zeros(pcamaxnums,pcamaxnums); for i=1:classnum SB=SB+everypersonnums*(everypersonmean(i,:)-pcastudy2mean)'*(everypersonmean(i,:)-pcastudy2mean); end %计算总类内散布矩阵Sw SW=zeros(pcamaxnums,pcamaxnums); for i=1:classnum for j=1:everypersonnums SW=SW+(pcastudy2((i-1)*everypersonnums+j,:)-everypersonmean(i,:))'*(pcastudy2((i-1)*everypersonnums+j,:)-everypersonmean(i,:)); end end %计算投影矩阵 bb=(inv(SW))*SB; [beignvectors,beignvalues] = eig(bb); %将特征值从大到小排列 % [bee,bkk]=sort(diag(beignvalues),'descend'); [bee,bkk]=sort(diag(beignvalues)); bkk=flipud(bkk); %取得排列后特征值对应的特征向量 beigns=beignvectors(:,bkk); wf(:,1:ldamaxnums)=beigns(:,1:ldamaxnums); %求pca在训练集上的投影矩阵 pcastudy2=pstudy1*UU; pcastudy2mean=mean(pcastudy2); pcastudy2=pcastudy2-pcastudy2mean(ones(mstudy,1),:); %求pca在测试集上的投影矩阵 pcatest2=ptest1*UU; pcatest2mean=mean(pcatest2); pcatest2=pcatest2-pcatest2mean(ones(mtest,1),:); learnfishstudy=pcastudy2*wf;% 计算训练样本的PCA+FLD特征向量 testfishstudy=pcatest2*wf;% 计算测试样本的PCA+FLD特征向量 %对pca训练集和测试集上的矩阵进行单位化 st1=learnfishstudy; % for i=1:size(st1) % st1(i,:)=st1(i,:)/norm(st1(i,:)); % end ldastudy=[st1 double(pstudy(:,nstudy))]; st2=testfishstudy; % for i=1:size(st2) % st2(i,:)=st2(i,:)/norm(st2(i,:)); % end ldatest=[st2 double(ptest(:,ntest))]; savefile = 'imageldadata.mat'; save(savefile, 'ldastudy','ldatest')