A Bayesian Matrix Factorization Model for Relational Data

@inproceedings{Singh2010ABM,
  title={A Bayesian Matrix Factorization Model for Relational Data},
  author={Ajit Paul Singh and Geoffrey J. Gordon},
  booktitle={UAI},
  year={2010}
}
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 MetropolisHastings sampler which uses the gradient and Hessian of the likelihood to dynamically tune the proposal… CONTINUE READING
Highly Cited
This paper has 52 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 31 extracted citations

Modeling Large Social Networks in Context

View 7 Excerpts
Method Support
Highly Influenced

Uncertainty Quantified Matrix Completion Using Bayesian Hierarchical Matrix Factorization

2014 13th International Conference on Machine Learning and Applications • 2014
View 4 Excerpts
Highly Influenced

A PAC bound for joint matrix completion based on Partially Collective Matrix Factorization

2016 23rd International Conference on Pattern Recognition (ICPR) • 2016
View 1 Excerpt

52 Citations

051015'11'13'15'17'19
Citations per Year
Semantic Scholar estimates that this publication has 52 citations based on the available data.

See our FAQ for additional information.