[jira] Assigned: (MAHOUT-180) port Hadoop-ified Lanczos SVD implementation from decomposer

["Jake Mannix (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAHOUT-180?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Jake Mannix reassigned MAHOUT-180:
----------------------------------

    Assignee: Jake Mannix

> port Hadoop-ified Lanczos SVD implementation from decomposer
> ------------------------------------------------------------
>
>                 Key: MAHOUT-180
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-180
>             Project: Mahout
>          Issue Type: New Feature
>          Components: Matrix
>    Affects Versions: 0.2
>            Reporter: Jake Mannix
>            Assignee: Jake Mannix
>            Priority: Minor
>
> I wrote up a hadoop version of the Lanczos algorithm for performing SVD on sparse matrices available at http://decomposer.googlecode.com/, which is Apache-licensed, and I'm willing to donate it.  I'll have to port over the implementation to use Mahout vectors, or else add in these vectors as well.
> Current issues with the decomposer implementation include: if your matrix is really big, you need to re-normalize before decomposition: find the largest eigenvalue first, and divide all your rows by that value, then decompose, or else you'll blow over Double.MAX_VALUE once you've run too many iterations (the L^2 norm of intermediate vectors grows roughly as (largest-eigenvalue)^(num-eigenvalues-found-so-far), so losing precision on the lower end is better than blowing over MAX_VALUE).  When this is ported to Mahout, we should add in the capability to do this automatically (run a couple iterations to find the largest eigenvalue, save that, then iterate while scaling vectors by 1/max_eigenvalue).

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