title: Multidimensional scaling with missing values in dissimilarity matrix
body: <p>I have a dissimilarity matrix on which I would like to perform multidimensional scaling (MDS) using the <a href="http://scikit-learn.org/stable/modules/generated/sklearn.manifold.MDS.html" rel="noreferrer">sklearn.manifold.MDS</a> function. The dissimilarity between some elements in this matrix is not meaningful and I am thus wondering if there is a way to run MDS on a sparse matrix or on a matrix with missing values? 
  According to <a href="https://stackoverflow.com/questions/17529646/nan-inf-values-in-scikit-learn-manifold-learning-functions">this</a> question, dissimilarities with 0 are considered as missing values, but I was unable to find this statement in the official documentation. Isn't a dissimilarity with value 0 interpreted as points that are very close to each other?</p>
  
  <p>Any suggestions how to obtain a lower-dimensional representation of my high-dimensional dataset based on a sparse dissimilarity matrix would be welcome. Thanks!</p>