Reducing dimensionality on training data with PCA in Matlab

This is a follow up question to:

PCA Dimensionality Reduction

In order to classify the new 10 dimensional test data do I have to reduce the training data down to 10 dimensions as well?

I tried:

X = bsxfun(@minus, trainingData, mean(trainingData,1));           
covariancex = (X'*X)./(size(X,1)-1);                 
[V D] = eigs(covariancex, 10);   % reduce to 10 dimension
Xtrain = bsxfun(@minus, trainingData, mean(trainingData,1));  
pcatrain = Xtest*V;

But using the classifier with this and the 10 dimensional testing data produces very unreliable results? Is there something that I am doing fundamentally wrong?

Edit:

X = bsxfun(@minus, trainingData, mean(trainingData,1));           
covariancex = (X'*X)./(size(X,1)-1);                 
[V D] = eigs(covariancex, 10);   % reduce to 10 dimension
Xtrain = bsxfun(@minus, trainingData, mean(trainingData,1));  
pcatrain = Xtest*V;

X = bsxfun(@minus, pcatrain, mean(pcatrain,1));           
covariancex = (X'*X)./(size(X,1)-1);                 
[V D] = eigs(covariancex, 10);   % reduce to 10 dimension
Xtest = bsxfun(@minus, test, mean(pcatrain,1));  
pcatest = Xtest*V;