Abstract
We describe a directed acyclic graphical model that contains a hierarchy of linear units and a mechanism for dynamically selecting an appropriate subset of these units to model each observation. The non-linear selection mechanism is a hierarchy of binary units each of which gates the output of one of the linear units. There are no connections from linear units to binary units, so the generative model can be viewed as a logistic belief net (Neal 1992) which selects a skeleton linear model from among the available linear units. We show that Gibbs sampling can be used to learn the parameters of the linear and binary units even when the sampling is so brief that the Markov chain is far from equilibrium.
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© 1998 Springer Science+Business Media Dordrecht
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Hinton, G.E., Sallans, B., Ghahramani, Z. (1998). A Hierarchical Community of Experts. In: Jordan, M.I. (eds) Learning in Graphical Models. NATO ASI Series, vol 89. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5014-9_17
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DOI: https://doi.org/10.1007/978-94-011-5014-9_17
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6104-9
Online ISBN: 978-94-011-5014-9
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