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.