title: Predicting LDA topics for new data
body: <p>It looks like this question has may have been asked a few times before (<a href="https://stackoverflow.com/questions/10483349/topic-modeling-how-do-i-use-my-fitted-lda-model-to-predict-new-topics-for-a-new">here</a> <a href="https://stackoverflow.com/questions/14875493/lda-with-topicmodels-how-can-i-see-which-topics-different-documents-belong-to?rq=1">and here</a>), but it has yet to be answered. I'm hoping this is due to the previous ambiguity of the question(s) asked, as indicated by comments. I apologize if I am breaking protocol by asking a simliar question again, I just assumed that those questions would not be seeing any new answers.</p>
  
  <p>Anyway, I am new to Latent Dirichlet Allocation and am exploring its use as a means of dimension reduction for textual data. Ultimately I would like extract a smaller set of topics from a very large bag of words and build a classification model using those topics as a few variables in the model. I've had success in running LDA on a training set, but the problem I am having is being able to predict which of those same topics appear in some other test set of data. I am using R's topicmodels package right now, but if there is another way to this using some other package I am open to that as well.</p>
  
  <p>Here is an example of what I am trying to do:</p>
  
  <pre><code>library(topicmodels)
  data(AssociatedPress)
  
  train &lt;- AssociatedPress[1:100]
  test &lt;- AssociatedPress[101:150]
  
  train.lda &lt;- LDA(train,5)
  topics(train.lda)
  
  #how can I predict the most likely topic(s) from "train.lda" for each document in "test"?
  </code></pre>