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

I enjoyed reading it. I require to study more on this topic. Thanks for sharing a nice info..Any way I'm going to subscribe for your feed and I hope you post again soon.

ReplyDeleteVery nice post. I just stumbled upon your blog and wanted to mention that I've truly enjoyed browsing your blog posts.

ReplyDeleteNice post

ReplyDeleteAnti Rayap

parfum

San Diego Hills

Kanopi

It's really informative and useful for me.

ReplyDeleteiklan gratis

Great blog indeed!

ReplyDeleteUSA Property Investment

Thanks for giving me the useful information. I think I need it!

ReplyDeletefriv4 | friv 4 school | kizi4 | kizi 4|unblockedgames unblocked games for kids

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?

ReplyDeleteYes, it is still very much relevant. In fact I believe more people are using it now than when I first posted the link.

ReplyDelete