An implementation of the randomized truncated SVD algorithm of Halko, Martinsson & Tropp (2009).
1 |
M |
a dense or sparse numeric matrix |
n |
an integer specifying the desired number of singular components. This argument must be specified and must satisfy |
q |
number of power iterations (Halko et al. recommend |
oversampling |
oversampling factor. The rSVD algorithm computes an approximate SVD factorization of rank |
transpose |
if |
verbose |
whether to display progress messages during execution |
This implementation of randomized truncated SVD is based on the randomized PCA algorithm (Halko et al. 2009, p. 9). The discussion in Sec. 4 and 5 of the paper shows that the same algorithm applies to the case where the columns of A are not centered (Algorithm 4.3 + Algorithm 5.1).
A list with components
u |
a matrix whose columns contain the first |
v |
a matrix whose columns contain the first |
d |
a vector containing the first |
Stefan Evert (http://purl.org/stefan.evert)
Halko, N., Martinsson, P. G., and Tropp, J. A. (2009). Finding structure with randomness: Stochastic algorithms for constructing approximate matrix decompositions. Technical Report 2009-05, ACM, California Institute of Technology.
svd
, dsm.projection
, sparsesvd
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