Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koller
A serious problem in learning probabilistic models is the presence of hid(cid:173) den variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce seemingly complex de(cid:173) pendencies among the latter. In recent years, much attention has been devoted to the development of algorithms for learning parameters, and in some cases structure, in the presence of hidden variables. In this pa(cid:173) per, we address the related problem of detecting hidden variables that interact with the observed variables. This problem is of interest both for improving our understanding of the domain and as a preliminary step that guides the learning procedure towards promising models. A very natural approach is to search for "structural signatures" of hidden variables - substructures in the learned network that tend to suggest the presence of a hidden variable. We make this basic idea concrete, and show how to integrate it with structure-search algorithms. We evaluate this method on several synthetic and real-life datasets, and show that it performs surpris(cid:173) ingly well.