title: What does it mean to have zero mean in the data?
body: <p>I'm trying to find ways to normalize my dataset (represented as a matrix with documents as rows and columns as features) and I came across a technique called feature scaling. I found a Wikipedia article on it <a href="http://en.wikipedia.org/wiki/Feature_scaling" rel="nofollow">here</a>.</p>
  
  <p>One of the methods listed is Standardization which says "Feature standardization makes the values of each feature in the data have zero-mean and unit-variance." What does that mean (no pun intended)?</p>
  
  <p>In this method, "we subtract the mean from each feature. Then we divide the values (mean is already subtracted) of each feature by its standard deviation." When they say 'subtract the mean', is it the mean of the entire matrix or the mean of the column pertaining to that feature? </p>
  
  <p>Also, if this feature scaling method is applied, does the mean not have to be subtracted from columns when performing Principal Component Analysis (PCA) on the data? </p>