title: sklearn decomposition top terms
body: <p>Is there a way I can determine the top features/terms for each cluster in while the data was decomposed?</p>
  
  <p>in th example from the sklearn documentation, the top terms are extracted by sorting the features and comparing with the vectorizer feature_names, both with the same number of features.</p>
  
  <p><a href="http://scikit-learn.org/stable/auto_examples/document_classification_20newsgroups.html" rel="nofollow">http://scikit-learn.org/stable/auto_examples/document_classification_20newsgroups.html</a></p>
  
  <p>I would like to know how to implement get_top_terms_per_cluster():</p>
  
  <pre><code>X = vectorizer.fit_transform(dataset)  # with m features
  X = lsa.fit_transform(X)  # reduce number of features to m'
  k_means.fit(X)
  get_top_terms_per_cluster()  # out of m features
  </code></pre>