18. The method as set forth in claim 17 , further comprising: generating an input feature representation comprising a distribution over the set of features for an input object which is not part of the training corpus; and classifying the input object respective to the topics of the set of topics using the clean topic model.
17. The method as set forth in claim 13 , wherein the inferring comprises: inferring the generative topic model by performing the latent generative topic model allocation using a collapsed Gibbs sampling algorithm with an IBP prior probability distribution configured such that the inferred topic model is a clean topic model in which each topic is represented by a distribution over a sub-set of the set of features selected by the IBP prior probability distribution.