Question about using PCA to select major features from dataset

Hello all,

I need to use Principal components analysis(PCA) to help select most
important features from a dataset.

The features of the dataset are as follows: <f1, f2, f3, f4, f5>
The total number of record is 100. So that the dataset matrix is of
dimension 100x5.

After I run the PCA and I use the top 3 eigenvectors
'SelectedFeatureVectors '(corresponding the 3 largest eigenvalues) to
generate new dataset as follows,

NewDataSet = SelectedFeatureVectors x RawDataAdjustWithMean.

Now the NewDataSet is of dimension 100 x 3.

So my question is how do I know which features are selected for the
top three most important features?
In other words, which two features from <f1, f2, f3, f4, f5> has been
filtered from the NewDataSet?


Thank you very much
-Jake


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