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  • $\begingroup$ I am not sure I understand the second option. The OP's problem is that test data has different features (and different number of features) than the training data. How can the test data be projected on the principal axes of the training set? Or do you suggest something else, and I misunderstood? $\endgroup$ Commented Nov 3, 2014 at 16:54
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    $\begingroup$ Unfortunately, you can not. Your model only works for features seen in training step. There's nothing it can say to you about new words because it's never seen them before. New words, mapped into new features, are ignored. If you want your model to consider new words, you have to re-calculate the model including the new sentences. $\endgroup$ Commented Nov 3, 2014 at 17:50
  • $\begingroup$ I am not the OP :) I understand what you saying in the comment, but I still do not understand the "second option" in your answer (which I would otherwise happily upvote). $\endgroup$ Commented Nov 3, 2014 at 18:06
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    $\begingroup$ The first option definitely isn't a choice, because that way the train set has information about the test set, so they aren't independent sets. My doubt was how to do the second option, because dimensionalities are different, therefore matrix multiplication can't be done. What I did was discard all the words that are in the test set and that weren't on the train set, and rearrange everything so the order are the same in each matrix. $\endgroup$ Commented Nov 6, 2014 at 18:41