On 08-04-2013, at 16:44, Andy Cooper <andy_coope...@yahoo.co.uk> wrote:
>
>
> Dear All,
>
> I need to perform a SVD on a very large data matrix, of dimension ~ 500,000 x
> 1,000 , and I am looking
> for an efficient algorithm that can perform an approximate (partial) SVD to
> extract on the order of the top 50
> right and left singular vectors.
>
> Would be very grateful for any advice on what R-packages are available to
> perform such a task, what the RAM requirement is, and indeed what would be
> the state-of-the-art in terms of numerical algorithms and programming
> language to use to accomplish this task.
Info found with package sos and findFn("svd") and scrolling through the list
for something relevant.
Have a look at package irlba.
It can work with dense matrices and sparse matrices as provided by package
Matrix, according to the documentation.
Berend
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