SAMPLE and TRAINING must have the same number of columns. after using pca

load featurs_T
load featurs_S
load Group_Train
load Group_Test
%{
cv_x=cov(Feat1);
[V,D] = eig(cv_x);
d=diag(D);
d=d(end:-1:1);
sm_d=cumsum(d) /sum(d);
idx=find(sm_d>0.99);
T=[V(:,end:-1:idx(1))]';
new_feat1=T*Feat1';
%TrainingSet= new_feat1';
new_feat2=T*Feat2';
%TestSet= new_feat2';

TrainingSet = new_feat1'; 
TestSet = new_feat2';
%}
Feat1= Feat1';
Feat2= Feat2';
Group_Train1 = Group_Train1';
Group_Test1 = Group_Test1';
%}
Feat1 = pca(Feat1);
Feat2 = pca (Feat2);
%------------------------

%}
% result1= multisvm(TrainingSet,Group_Train1,TestSet,Group_Test1);
result= classify(Feat2,Feat1,Group_Train1,'diaglinear');

%testresult = result1;

Accuracy = mean(Group_Test1==result) * 100;
fprintf('Accuracy = %.2f\n', Accuracy);
fprintf('error rate = %.2f\n ', mean(result ~= Group_Test1 ) * 100);

Error using classify (line 153)

SAMPLE and TRAINING must have the same number of columns.

Error in HOG2 (line 32)

result= classify(Feat2,Feat1,Group_Train1,'diaglinear');

After using pca the matrix dimensions change

Feat1= 1032*1032

Feat2 =109*109

result = 1032*1