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classify_RSPCA.m
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classify_RSPCA.m
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function classify_RSPCA(iRho)
% Calculate the classification accuracy of RSPCA.
% 2018-4-23 18:33:36
load sInfo.mat;
fprintf('classify_RSPCA(%s,%d)\n\n',FaceDB,iRho);
load(sprintf('data/%s.mat',FaceDB));
% reshape 2D images to vectors
x=reshape(x,[height*width,nImg]);
accuracy=zeros(nPV,nRep);
tic;
for iRep=1:nRep
load(sprintf('data/%s_r%d.mat',FaceDB,iRep));
ix_train=1-ix_test;
ix_train=find(ix_train);
ix_test=find(ix_test);
num_train=length(ix_train);
num_test=length(ix_test);
x_train=x(:,ix_train);
x_test=x(:,ix_test);
label_train=label(ix_train);
label_test=label(ix_test);
% subtract the mean
x_mean=mean(x_train,2);
x_train=x_train-repmat(x_mean,[1,num_train]);
x_test=x_test-repmat(x_mean,[1,num_test]);
Rho=sRho(iRho);
W=RSPCA(x_train,Rho,nPV); % RSPCA
% reserve the result after projection
x_train_reserve=W'*x_train;
x_test_reserve=W'*x_test;
for iPV=1:nPV
x_train_proj=x_train_reserve(1:iPV,:);
x_test_proj=x_test_reserve(1:iPV,:);
% nearest neighbor classifier
dxx=pdist2(x_train_proj',x_test_proj');
[~,ix]=min(dxx);
label_predict=label_train(ix);
accuracy(iPV,iRep)=mean(label_predict==label_test);
end
perct(toc,iRep,nRep);
end
time=toc/60;
accuracy=mean(accuracy,2);
save(sprintf('result/classify_RSPCA_%s_iRho%d.mat',FaceDB,iRho),'accuracy','time');