svn commit: r1006183 - in /websites/staging/mahout/trunk/content: ./ users/algorithms/d-spca.html

[buildbot]

Author: buildbot
Date: Sat Feb  4 00:48:54 2017
New Revision: 1006183

Log:
Staging update by buildbot for mahout

Modified:
    websites/staging/mahout/trunk/content/   (props changed)
    websites/staging/mahout/trunk/content/users/algorithms/d-spca.html

Propchange: websites/staging/mahout/trunk/content/
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--- cms:source-revision (original)
+++ cms:source-revision Sat Feb  4 00:48:54 2017
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Modified: websites/staging/mahout/trunk/content/users/algorithms/d-spca.html
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--- websites/staging/mahout/trunk/content/users/algorithms/d-spca.html 
(original)
+++ websites/staging/mahout/trunk/content/users/algorithms/d-spca.html Sat Feb  
4 00:48:54 2017
@@ -281,7 +281,7 @@
 h2:hover > .headerlink, h3:hover > .headerlink, h1:hover > .headerlink, 
h6:hover > .headerlink, h4:hover > .headerlink, h5:hover > .headerlink, 
dt:hover > .elementid-permalink { visibility: visible }</style>
 <h1 id="distributed-stochastic-pca">Distributed Stochastic PCA<a 
class="headerlink" href="#distributed-stochastic-pca" title="Permanent 
link">&para;</a></h1>
 <h2 id="intro">Intro<a class="headerlink" href="#intro" title="Permanent 
link">&para;</a></h2>
-<p>Mahout has a distributed implementation of Stochastic PCA[1]. This 
algorithm computes the exact equivalent of Mahout's 
dssvd(<code>\(\mathbf{A-1\mu}\)</code>) by modifying the <code>dssvd</code> 
algorithm so as to avoid forming <code>\(\mathbf{A-1\mu}\)</code>, which would 
densify a sparse input. Thus, it is suitable for work with both dense and 
sparse inputs.</p>
+<p>Mahout has a distributed implementation of Stochastic PCA[1]. This 
algorithm computes the exact equivalent of Mahout's 
dssvd(<code>\(\mathbf{A-1\mu^\top}\)</code>) by modifying the 
<code>dssvd</code> algorithm so as to avoid forming 
<code>\(\mathbf{A-1\mu^\top}\)</code>, which would densify a sparse input. 
Thus, it is suitable for work with both dense and sparse inputs.</p>
 <h2 id="algorithm">Algorithm<a class="headerlink" href="#algorithm" 
title="Permanent link">&para;</a></h2>
 <p>Given an <em>m</em> <code>\(\times\)</code> <em>n</em> matrix 
<code>\(\mathbf{A}\)</code>, a target rank <em>k</em>, and an oversampling 
parameter <em>p</em>, this procedure computes a <em>k</em>-rank PCA by finding 
the unknowns in <code>\(\mathbf{A−1\mu^\top \approx U\Sigma 
V^\top}\)</code>:</p>
 <ol>