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From "Sean Owen (JIRA)" <j...@apache.org>
Subject [jira] Commented: (MAHOUT-121) Speed up distance calculations for sparse vectors
Date Sat, 13 Jun 2009 07:57:07 GMT

    [ https://issues.apache.org/jira/browse/MAHOUT-121?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12719094#action_12719094
] 

Sean Owen commented on MAHOUT-121:
----------------------------------

Good point on the overflow, even if it would only happen after the  vector had over a billion
non-zero elements, taking 12GB+ of memory! Arrays.binarySearch() has this issue, so perhaps
good to just keep a fixed version of binary search.

Yes there needs to be a way to iterate over the elements directly, quickly. Thinking it best
to just return the arrays, if we're going for speed.

Would it be useful to take a shot at rewriting SparseVector to use this?

> Speed up distance calculations for sparse vectors
> -------------------------------------------------
>
>                 Key: MAHOUT-121
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-121
>             Project: Mahout
>          Issue Type: Improvement
>          Components: Matrix
>            Reporter: Shashikant Kore
>         Attachments: mahout-121.patch
>
>
> From my mail to the Mahout mailing list.
> I am working on clustering a dataset which has thousands of sparse vectors. The complete
dataset has few tens of thousands of feature items but each vector has only couple of hundred
feature items. For this, there is an optimization in distance calculation, a link to which
I found the archives of Mahout mailing list.
> http://lingpipe-blog.com/2009/03/12/speeding-up-k-means-clustering-algebra-sparse-vectors/
> I tried out this optimization.  The test setup had 2000 document  vectors with few hundred
items.  I ran canopy generation with Euclidean distance and t1, t2 values as 250 and 200.
>  
> Current Canopy Generation: 28 min 15 sec.
> Canopy Generation with distance optimization: 1 min 38 sec.
> I know by experience that using Integer, Double objects instead of primitives is computationally
expensive. I changed the sparse vector  implementation to used primitive collections by Trove
[
> http://trove4j.sourceforge.net/ ].
> Distance optimization with Trove: 59 sec
> Current canopy generation with Trove: 21 min 55 sec
> To sum, these two optimizations reduced cluster generation time by a 97%.
> Currently, I have made the changes for Euclidean Distance, Canopy and KMeans.  
> Licensing of Trove seems to be an issue which needs to be addressed.

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