title: Performing PCA on a large dataset
body: <p>I've got a document classification problem with only 2 classes and my training dataset matrix size, after the CountVectorizer becomes (40,845 X 218,904) (unigram). In the case of considering trigrams, it can reach up to (40845 X 3,931,789). Is there a way to perform PCA on such dataset without getting memory or sparse dataset errors. I'm using python sklearn on an 6GB machine.</p>