title: How to deal with missing values in python scikit NMF
body: <p>I am trying to apply NMF on my dataset, using python scikit-learn. My dataset contains 0 values and missing values. But scikit-learn does not allow NaN value in data matrix. Some posts said that replace missing values with zeros. </p>
  
  <p>my questions are:</p>
  
  <ul>
  <li><p>If I replace missing value with zeros, how can the algorithm tell the missing values and real zero values?</p></li>
  <li><p>Is there any other NMF implementations can deal with missing values?</p></li>
  <li><p>Or if there are any other matrix factorization algorithms can do the missing value prediction?</p></li>
  </ul>