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Feature Extraction through PCA

I'm trying to select a subset of features from a data that contains 2000 of them for 63 samples. Now I know how to do PCA in MATLAB. I used 'pcacov' and it returns the eigenvectors and the eigenvalues too. However, I don't know how to select the features I want. I mean if the features aren't labeled, how can I select my features ? or they will be returned in the same order ?

Solution

  • how can I select my features ?

    If you call it like

    [pc,variances,explained] = pcacov(covx)

    then the principal components are the vectors in the first return argument with variances as in the second return argument. They are in correspondence and sorted from most significant to least significant.

    or they will be returned in the same order ?

    You can assume this if the function help says so, otherwise it's not safe to assume so and you can do something like.

    [varsorted,varsortedinds] = sort(variances,'descend');
pcsorted = pc(:,varsortedinds);

    And varsorted and pcsorted will be in order from most to least significant.

    Edit 7 years later: I realized in re-reading the question that my answer doesn't actually answer this. I thought what was being asked was are the principal components sorted. Don Reba's answer is an answer to the actual question asked. I can't delete a selected answer though.