title: Deciding about dimensionality reduction with PCA
body: <p>I have 2D data (I have a zero mean normalized data). I know the covariance matrix, eigenvalues and eigenvectors of it. I want to decide whether to reduce the dimension to 1 or not (I use principal component analysis, PCA). How can I decide?  Is there any methodology for it?</p>
  
  <p>I am looking sth. like if you look at this ratio and if this ratio is high than it is logical to go on with dimensionality reduction.</p>
  
  <p><strong>PS 1:</strong> Does PoV (Proportion of variation) stands for it?</p>
  
  <p><strong>PS 2:</strong> Here is an answer: <a href="https://stats.stackexchange.com/questions/22569/pca-and-proportion-of-variance-explained">https://stats.stackexchange.com/questions/22569/pca-and-proportion-of-variance-explained</a> does it a criteria to test it?</p>