Prepare data for SVM, Is it valid to normalise the data before and after PCA dimension reduction Is it valid to normalise a dataset, reduce dimensionality with PCA and then to normalise the reduced dimension data.    Assuming this is performed on training data, should the same PCA coefficients be used to reduce the dimension of the test data.  Should the same max and min normalisation values be used for the test and training data. I have included a simplified example of the code I am using which may describe I said better. Thanks in advance. 

%% Prepare Training Data


% Normalise training data
mindata=min(TRAINDATA); maxdata=max(TRAINDATA);
TRAINDATA = ((TRAINDATA-repmat(mindata,[size(TRAINDATA,1),1]))./(repmat(maxdata,[size(TRAINDATA,1),1])-repmat(mindata,[size(TRAINDATA,1),1])) - 0.5 ) *2;

% Perform PCA
mTRAINDATA = mean(mean(TRAINDATA));
TRAINDATA = TRAINDATA - mTRAINDATA;
[Cpca,~,~,~,~]=princomp(TRAINDATA,'econ');
EigenRange = 1:2;
Cpca = Cpca(:,EigenRange);
TRAINDATA = TRAINDATA*Cpca;
TRAINDATA = TRAINDATA + mTRAINDATA;

% Normalise training data second time
mindata2=min(TRAINDATA); maxdata2=max(TRAINDATA);
TRAINDATA = ((TRAINDATA-repmat(mindata2,[size(TRAINDATA,1),1]))./(repmat(maxdata2,[size(TRAINDATA,1),1])-repmat(mindata2,[size(TRAINDATA,1),1])) - 0.5 ) *2;



%% Prepare Test Data

% Normalise using first normalisation values from training data
TESTDATA = ((TESTDATA-repmat(mindata,[size(TESTDATA,1),1]))./(repmat(maxdata,[size(TESTDATA,1),1])-repmat(mindata,[size(TESTDATA,1),1])) - 0.5 ) *2;

% Perform PCA
mTESTDATA = mean(mean(TESTDATA));
TESTDATA = TESTDATA - mTESTDATA;
TESTDATA = TESTDATA*Cpca;
TESTDATA = TESTDATA + mTRAINDATA;

% Normalise using second normalisation values from training data
TESTDATA = ((TESTDATA-repmat(mindata2,[size(TESTDATA,1),1]))./(repmat(maxdata2,[size(TESTDATA,1),1])-repmat(mindata2,[size(TESTDATA,1),1])) - 0.5 ) *2;
Depending on the language of your choice, you may or may not have to normalise the data yourself - for example the e1071 package in R does this automatically for you. As this is built off the 'libsvm' library, it might be that this is also the case there - the library documentation is your best source. As for the normalisation values, you should definitely use the same [min,max] values from the training set for the test set as well. Also, I would reccomend reducing the dimensionality of your data first, than normalising before running a svm. hth