• Computer Science, Mathematics
  • Published in UAI 2010

A Bayesian Matrix Factorization Model for Relational Data

@article{Singh2010ABM,
  title={A Bayesian Matrix Factorization Model for Relational Data},
  author={Ajit Paul Singh and Geoffrey J. Gordon},
  journal={ArXiv},
  year={2010},
  volume={abs/1203.3517}
}
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… CONTINUE READING

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