1. A non-transitory storage medium storing instructions executable by a processor to perform a method comprising: generating feature representations comprising distributions over a set of features corresponding to objects of a training corpus of objects; and inferring a topic model defining a set of topics by performing latent Dirichlet allocation (LDA) with an Indian Buffet Process (IBP) compound Dirichlet prior probability distribution, the inferring being performed using a collapsed Gibbs sampling algorithm by iteratively sampling (1) topic allocation variables of the LDA and (2) binary activation variables of the IBP compound Dirichlet prior probability distribution; wherein the inferring performed using a collapsed Gibbs sampling algorithm does not iteratively sample any parameters other than topic allocation variables of the LDA and binary activation variables of the IBP compound Dirichlet prior probability distribution.
1. A non-transitory storage medium storing instructions executable by a processor to perform a method comprising: generating feature representations comprising distributions over a set of features corresponding to objects of a training corpus of objects; and inferring a topic model defining a set of topics by performing latent Dirichlet allocation (LDA) with an Indian Buffet Process (IBP) compound Dirichlet prior probability distribution, the inferring being performed using a collapsed Gibbs sampling algorithm by iteratively sampling (1) topic allocation variables of the LDA and (2) binary activation variables of the IBP compound Dirichlet prior probability distribution; wherein the inferring performed using a collapsed Gibbs sampling algorithm does not iteratively sample any parameters other than topic allocation variables of the LDA and binary activation variables of the IBP compound Dirichlet prior probability distribution. 12. 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 (i) the inferred topic model associates a focused sub-set of the set of topics to each object of the training corpus and (ii) the inferred topic model is a clean topic model defining each topic as a distribution over a sub-set of the set of features selected by the IBP compound Dirichlet prior probability distribution.