Uncertainty in Artificial Intelligence
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A Bayesian Matrix Factorization Model for Relational Data
Ajit Singh, Geoffrey Gordon
Abstract:
Relational learning can be used to augment one data source with other correlated sources of information, to improve predictive accuracy. We frame a large class of relational learning problems as matrix factorization problems, and propose a hierarchical Bayesian model. Training our Bayesian model using random-walk Metropolis-Hastings is impractically slow, and so we develop a block Metropolis- Hastings sampler which uses the gradient and Hessian of the likelihood to dynamically tune the proposal. We demonstrate that a predictive model of brain response to stimuli can be improved by augmenting it with side information about the stimuli.
Keywords:
Pages: 556-563
PS Link:
PDF Link: /papers/10/p556-singh.pdf
BibTex:
@INPROCEEDINGS{Singh10,
AUTHOR = "Ajit Singh and Geoffrey Gordon",
TITLE = "A Bayesian Matrix Factorization Model for Relational Data",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "556--563"
}


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