主分量分析投影matlab实现

function [ ProjectedTrainSamples,ProjectedTestSamples ] = PCAProject ( TrainSamples ,TestSamples,ProNum ) %Function ---求取TrainSamples和TestSamples的ProNum维PCA投影特征，并进行归一化 %TrainSamples ---样本（每个样本为列向量） %TestSamples ---样本（每个样本为列向量） %ProNum ---PCA投影轴数 %ProjectedTrainSamples ---投影后的训练样本特征 %ProjectedTestSamples ---投影后的测试样本特征 %Author ---周强 南京理工大学603-2教研室 moxibingdao@qq.com disp('计算PCA中.....'); SampleMean = mean(TrainSamples,2); for i = 1:size(TrainSamples,2), TrainSamples(:,i) = TrainSamples(:,i) - SampleMean; end for i = 1:size(TestSamples,2), TestSamples(:,i) = TestSamples(:,i) - SampleMean; end; %%%%%%%%%%对图像矢量TrainImage1进行ＫＬ变换%%%%%%%%%%%% Compress = KL(TrainSamples,ProNum); ProjectedTrainSamples = TrainSamples'*Compress; ProjectedTestSamples = TestSamples'*Compress; %%%%%%%%%%%%%%%%%%%%%%归一化%%%%%%%%%%%%%%%%%%%%%%%%%% s1=size(ProjectedTrainSamples,1); ProjectedTrainSamples=ProjectedTrainSamples'; for i=1:1:s1 a=ProjectedTrainSamples(:,i); b=a/norm(a); ProjectedTrainSamples(:,i)=b; end s1=size(ProjectedTestSamples,1); ProjectedTestSamples=ProjectedTestSamples'; for i=1:1:s1 a=ProjectedTestSamples(:,i); b=a/norm(a); ProjectedTestSamples(:,i)=b; end