svn commit: r1006172 - in /websites/staging/mahout/trunk/content: ./ users/algorithms/d-spca.html

[bu...@apache.org]

Author: buildbot
Date: Sat Feb  4 00:02:06 2017
New Revision: 1006172

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/
------------------------------------------------------------------------------
--- cms:source-revision (original)
+++ cms:source-revision Sat Feb  4 00:02:06 2017
@@ -1 +1 @@
-1781626
+1781627

Modified: websites/staging/mahout/trunk/content/users/algorithms/d-spca.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/algorithms/d-spca.html (original)
+++ websites/staging/mahout/trunk/content/users/algorithms/d-spca.html Sat Feb  4 00:02:06 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].</p>
+<p>Mahout has a distributed implementation of Stochastic PCA[1]. this algorithm computes the exact equivalent of Mahout's <code>dssvd(\(\mathbf{A-1\mu}\))</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>
 <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>\(\mathbf{A−1\mu^\top \approx U\Sigma V}\)</code>:</p>
 <ol>