Principal component analysis in Python

Question

I\'d like to use principal component analysis (PCA) for dimensionality reduction. Does numpy or scipy already have it, or do I have to roll my own using numpy.linalg.eigh?<

Answer 1

SVD should work fine with 460 dimensions. It takes about 7 seconds on my Atom netbook. The eig() method takes more time (as it should, it uses more floating point operations) and will almost always be less accurate.

If you have less than 460 examples then what you want to do is diagonalize the scatter matrix (x - datamean)^T(x - mean), assuming your data points are columns, and then left-multiplying by (x - datamean). That might be faster in the case where you have more dimensions than data.