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From build...@apache.org
Subject svn commit: r900525 - in /websites/staging/mahout/trunk/content: ./ users/dim-reduction/ssvd.html
Date Sat, 08 Mar 2014 06:18:24 GMT
Author: buildbot
Date: Sat Mar  8 06:18:24 2014
New Revision: 900525

Log:
Staging update by buildbot for mahout

Modified:
    websites/staging/mahout/trunk/content/   (props changed)
    websites/staging/mahout/trunk/content/users/dim-reduction/ssvd.html

Propchange: websites/staging/mahout/trunk/content/
------------------------------------------------------------------------------
--- cms:source-revision (original)
+++ cms:source-revision Sat Mar  8 06:18:24 2014
@@ -1 +1 @@
-1575489
+1575490

Modified: websites/staging/mahout/trunk/content/users/dim-reduction/ssvd.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/dim-reduction/ssvd.html (original)
+++ websites/staging/mahout/trunk/content/users/dim-reduction/ssvd.html Sat Mar  8 06:18:24
2014
@@ -294,15 +294,15 @@ As of 0.7 trunk, includes PCA and dimens
 map-reduce characteristics: 
 SSVD uses at most 3 MR sequential steps (map-only + map-reduce + 2 optional parallel map-reduce
jobs) to produce reduced rank approximation of U, V and S matrices. Additionally, two more
map-reduce steps are added for each power iteration step if requested.</p>
 <p><strong>Potential drawbacks:</strong></p>
-<p>potentially less precise (but adding even one power iteration seems to fix that
quite a bit).
-Documentation
-Overview and Usage
+<p>potentially less precise (but adding even one power iteration seems to fix that
quite a bit).</p>
+<p><strong>Documentation</strong></p>
+<p><a href="ssvd.page/ssvd.pdf">Overview and Usage</a>
 Note: Please use 0.6 or later! for PCA workflow, please use 0.7 or later.</p>
 <p><strong>Publications</strong></p>
 <p><a href="http://amath.colorado.edu/faculty/martinss/Pubs/2012_halko_dissertation.pdf">Nathan
Halko's dissertation</a> "Randomized methods for computing low-rank
 approximations of matrices" contains comprehensive definition of parallelization strategy
taken in Mahout SSVD implementation and also some precision/scalability benchmarks, esp. w.r.t.
Mahout Lanczos implementation on a typical corpus data set.</p>
 <p><strong>R simulation</strong></p>
-<p>Non-parallel SSVD simulation in R with power iterations and PCA options. Note that
this implementation is not most optimal for sequential flow solver, but it is for demonstration
purposes only.</p>
+<p><a href="ssvd.page/ssvd.R">Non-parallel SSVD simulation in R</a> with
power iterations and PCA options. Note that this implementation is not most optimal for sequential
flow solver, but it is for demonstration purposes only.</p>
 <p>However, try this R code to simulate a meaningful input:</p>
 <div class="codehilite"><pre>   tests.R
 n<span class="o">&lt;-</span><span class="m">1000</span>



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