title: importance of PCA or SVD in machine learning
body: <p>All this time (specially in Netflix contest), I always come across this blog (or leaderboard forum) where they mention how by applying a simple SVD step on data helped them in reducing sparsity in data or in general improved the performance of their algorithm in hand.
  I am trying to think (since long time) but I am not able to guess why is it so.
  In general, the data in hand I get is very noisy (which is also the fun part of bigdata) and then I do know some basic feature scaling stuff like log-transformation stuff , mean normalization.
  But how does something like SVD helps.
  So lets say i have a huge matrix of user rating movies..and then in this matrix, I implement some version of recommendation system (say collaborative filtering):</p>
  
  <pre><code>1) Without SVD
  2) With SVD
  </code></pre>
  
  <p>how does it helps</p>