title: Principal component analysis (PCA) assumptions
body: <p>I used PCA to reduce a 180 dimensions feature space in 3 principal components.
  Afterwards I used k-mean clustering to cluster the data according to the 3 principal components of PCA.</p>
  
  <p>I read in wikipedia that principal components are guaranteed to be independent if the data set is jointly normally distributed. I didn't calculate the jointly distribution of all my features (180)...is that a problem?</p>
  
  <p>Which are the assumptions (if any) or the best practices in using PCA for dimensionality reductions?</p>