numpy svd: is there a way to find only the first singular vectors instead of doing full svd?

Question

numpy.linalg.svd function gives the full svd of the input matrix. However I want only the first singular vectors.

I was wondering if there is any function in numpy for that or any other library in python?

score 3 · Answer 1 · answered Apr 15 '16 at 07:46

3

from sklearn.utils.extmath import randomized_svd
U, S, Vt = randomized_svd(X, n_components=1)

answered Apr 15 '16 at 07:46

JARS

score 3 · Answer 2 · answered Apr 16 '16 at 14:24

3

In addition to randomized svd, you can run ARPACK on the squared problem, via scipy.sparse.linalg.svds.

answered Apr 16 '16 at 14:24

pv.

2 Answers2