Principal component analysis in Python

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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?

I don't just want to use singular value decomposition (SVD) because my input data are quite high-dimensional (~460 dimensions), so I think SVD will be slower than computing the eigenvectors of the covariance matrix.

I was hoping to find a premade, debugged implementation that already makes the right decisions for when to use which method, and which maybe does other optimizations that I don't know about.

pca scipy numpy python
11 Answers

If you're working with 3D vectors, you can apply SVD concisely using the toolbelt vg. It's a light layer on top of numpy.

import numpy as np
import vg

vg.principal_components(data)

There's also a convenient alias if you only want the first principal component:

vg.major_axis(data)

I created the library at my last startup, where it was motivated by uses like this: simple ideas which are verbose or opaque in NumPy.

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