10. The non-transitory storage medium as set forth in claim 1 , wherein the performing LDA comprises: performing LDA with an IBP compound Dirichlet prior probability distribution configured such that the inferred topic model is a clean topic model in which each topic is defined as a distribution over a sub-set of the set of features selected by the IBP compound Dirichlet prior probability distribution.
10. The non-transitory storage medium as set forth in claim 1 , wherein the performing LDA comprises: performing LDA with an IBP compound Dirichlet prior probability distribution configured such that the inferred topic model is a clean topic model in which each topic is defined as a distribution over a sub-set of the set of features selected by the IBP compound Dirichlet prior probability distribution. 11. The non-transitory storage medium as set forth in claim 10 , wherein the method further comprises: 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.