TY - GEN
T1 - Discovering hidden variables
T2 - 14th Annual Neural Information Processing Systems Conference, NIPS 2000
AU - Elidan, Gal
AU - Lotner, Noam
AU - Friedman, Nir
AU - Koller, Daphne
PY - 2001
Y1 - 2001
N2 - A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce seemingly complex dependencies 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 paper, 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 surprisingly well.
AB - A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As such, they induce seemingly complex dependencies 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 paper, 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 surprisingly well.
UR - http://www.scopus.com/inward/record.url?scp=84898950733&partnerID=8YFLogxK
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AN - SCOPUS:84898950733
SN - 0262122413
SN - 9780262122412
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 13 - Proceedings of the 2000 Conference, NIPS 2000
PB - Neural information processing systems foundation
Y2 - 27 November 2000 through 2 December 2000
ER -