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I have a dataset with both continuous and categorical features. I want to reduce the dimensionality, but cannot apply PCA directly on the dataset because of the categorical features.

One solution I thought of was to run PCA exclusively on the continuous features, reduce the dimensions there, and then add the categorical features as they are to the reduced table with the continuous features.

I have not seen this method anywhere, but it makes sense to me, so I was wondering if it's OK.

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marked as duplicate by whuber Jun 14 '17 at 20:28

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    $\begingroup$ that really depends on what type of data/problem you have $\endgroup$ – redress Jun 14 '17 at 18:36
  • $\begingroup$ @redress can you please elaborate. Right now, I just want to reduce features. What I'm suggesting is that all categorical features stay, but at least I can reduce the number of continuous features, even if they lose their interpretability. $\endgroup$ – lhay86 Jun 14 '17 at 19:13
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    $\begingroup$ how many different classes exist in each category? what is the actual complexity of the categorical data? $\endgroup$ – redress Jun 14 '17 at 19:40
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You can binaries your categorical features. Try to google and learn about One-of-k coding. That way you end up with only numerical data.

And if you do that I will suggest that you standardize your numerical features.

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  • $\begingroup$ stats.stackexchange.com/questions/5774/… My way is being criticized here. Worth a read $\endgroup$ – k.dkhk Jun 14 '17 at 18:45
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    $\begingroup$ Even if I binarize categorical features, I will end up with discrete binary features (which, while numerical, are not continuous and therefore not naturally used in PCA) $\endgroup$ – lhay86 Jun 14 '17 at 19:11
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    $\begingroup$ Could you state a reference of PCA requiring continuous data? $\endgroup$ – Michael M Jun 14 '17 at 20:27

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