Can PCA be applied to reduce dimensionality of only a subset of features?

Lets say I have a feature set of f0 to f1000. I am thinking of applying PCA on f500 to f1000 reducing their dimensionality. Can I combine this reduced set with the features f0 to f499 as the feature space for training a learning algorithm?

• Just curious, for educational purposes, why you are interested in applying PCA to a feature subspace and how you choose which ones? I think it is useful to share your experience and edit your question such that other learn. Feb 7 '18 at 10:59
• The subset of features in my problem, from f500 to f1000 are sparse and are of Boolean type. Hence I am interested in trying out PCA to find a reduced set. Feb 7 '18 at 18:26