Patent Document ID: 8510257
Application ID: 12907219

Base Claim:
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.

---

Claim 2:
2. The non-transitory storage medium as set forth in claim 1 , wherein the objects comprise documents including text, the feature representations comprise bag-of-words representations, and the set of features comprises a set of vocabulary words.