Daniel M. Roy, Charles Kemp, Vikash Mansinghka, Joshua Tenenbaum
The objects in many real-world domains can be organized into hierarchies, where each internal node picks out a category of objects. Given a collection of fea- tures and relations deﬁned over a set of objects, an annotated hierarchy includes a speciﬁcation of the categories that are most useful for describing each individual feature and relation. We deﬁne a generative model for annotated hierarchies and the features and relations that they describe, and develop a Markov chain Monte Carlo scheme for learning annotated hierarchies. We show that our model discov- ers interpretable structure in several real-world data sets.