Uncertainty in Artificial Intelligence
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Variational Learning in Mixed-State Dynamic Graphical Models
Vladimir Pavlovic, Brendan Frey, Thomas Huang
Abstract:
Many real-valued stochastic time-series are locally linear (Gassian), but globally non-linear. For example, the trajectory of a human hand gesture can be viewed as a linear dynamic system driven by a nonlinear dynamic system that represents muscle actions. We present a mixed-state dynamic graphical model in which a hidden Markov model drives a linear dynamic system. This combination allows us to model both the discrete and continuous causes of trajectories such as human gestures. The number of computations needed for exact inference is exponential in the sequence length, so we derive an approximate variational inference technique that can also be used to learn the parameters of the discrete and continuous models. We show how the mixed-state model and the variational technique can be used to classify human hand gestures made with a computer mouse.
Keywords:
Pages: 522-530
PS Link:
PDF Link: /papers/99/p522-pavlovic.pdf
BibTex:
@INPROCEEDINGS{Pavlovic99,
AUTHOR = "Vladimir Pavlovic and Brendan Frey and Thomas Huang",
TITLE = "Variational Learning in Mixed-State Dynamic Graphical Models",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "522--530"
}


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