rsvd {wordspace} | R Documentation |
Randomized Singular Value Decomposition (wordspace)
Description
An implementation of the randomized truncated SVD algorithm of Halko, Martinsson & Tropp (2009).
Usage
rsvd(M, n, q = 2, oversampling = 2, transpose = FALSE, verbose = FALSE)
Arguments
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 |
Details
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).
Value
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 |
Author(s)
Stephanie Evert (https://purl.org/stephanie.evert)
References
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.
See Also
svd
, dsm.projection
, sparsesvd