Author: akm
Date: Fri Feb 3 23:13:18 2017
New Revision: 1781617
URL: http://svn.apache.org/viewvc?rev=1781617&view=rev
Log:
Formatting
Modified:
mahout/site/mahout_cms/trunk/content/users/algorithms/d-spca.mdtext
Modified: mahout/site/mahout_cms/trunk/content/users/algorithms/d-spca.mdtext
URL:
http://svn.apache.org/viewvc/mahout/site/mahout_cms/trunk/content/users/algorithms/d-spca.mdtext?rev=1781617&r1=1781616&r2=1781617&view=diff
==============================================================================
--- mahout/site/mahout_cms/trunk/content/users/algorithms/d-spca.mdtext
(original)
+++ mahout/site/mahout_cms/trunk/content/users/algorithms/d-spca.mdtext Fri Feb
3 23:13:18 2017
@@ -8,6 +8,7 @@ Mahout has a distributed implementation
## Algorithm
Given an *m* `\(\times\)` *n* matrix `\(\mathbf{A}\)`, a target rank *k*, and
an oversampling parameter *p*, this procedure computes a *k*-rank PCA by
finding the unknowns in `\(\mathbf{Aâ1\mu^\top \ge U\Sigma V}\)`:
+
1. Create seed for random *n* `\(\times\)` *(k+p)* matrix `\(\Omega\)`.
2. `\(s_\Omega \leftarrow \Omega^\top \mu\)`.
3. `\(\mathbf{Y_0} \leftarrow A\Omega â 1(s_\Omega)^\top, Y \in
\mathbb{R}^(m\times(k+p))\)`.
