Your verification ID is: guDlT7MCuIOFFHSbB3jPFN5QLaQ Big Computing: Bryan Lewis's Vignette on IRLBA for SVD in R

Tuesday, May 31, 2011

Bryan Lewis's Vignette on IRLBA for SVD in R

The Implicitly Restarted Lanczos Bidiagonalization Algorithm (IRLBA) of Jim Baglama and Lothar Reichel is a state of the art method for computing a few singular vectors and corresponding singular values of huge matrices.

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 competitions have really brought powerful tools like SVD into greater use.

Video Vignette or IRLBA using the Netflix Prize data set.


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

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

    Here is the documentation:

  3. 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.

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  5. 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?

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

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