The

**IRLBA**package is the R language implementation of the method. With it, you can compute partial SVDs and principal component analyses of very large scale data. The package works well with sparse matrices and with other matrix classes like those provided by the Bigmemory package.

In Video Vignette Link I have inserted below Bryan with a new microphone goes through an example using this package on the Netflix Prize data set (480K row by 18K columns). Competitions like the Netflix Prize and the Kaggle.com competitions have really brought powerful tools like SVD into greater use.

Video Vignette or IRLBA using the Netflix Prize data set.

How much time it took to find eigenvector matrices & what k(rank) you used for the same ?

ReplyDeleteIt is on one of the slides. On an old AMD Opteron Server it took about 120sec with nu=5 nv=5.

ReplyDeleteHere is the documentation:

www.rforge.net/src/contrib/Documentation/irlba.pdf

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Hey, thanks for that link! Is the IRLBA still relevant? I stumbled across this while looking for information about algorithms in financial services analytics solutions. Any thoughts on how this could apply?

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