A Bayesian Matrix Factorization Model for Relational Data
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.
Cite
Text
Singh and Gordon. "A Bayesian Matrix Factorization Model for Relational Data." Conference on Uncertainty in Artificial Intelligence, 2010.Markdown
[Singh and Gordon. "A Bayesian Matrix Factorization Model for Relational Data." Conference on Uncertainty in Artificial Intelligence, 2010.](https://mlanthology.org/uai/2010/singh2010uai-bayesian/)BibTeX
@inproceedings{singh2010uai-bayesian,
title = {{A Bayesian Matrix Factorization Model for Relational Data}},
author = {Singh, Ajit Paul and Gordon, Geoffrey J.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2010},
pages = {556-563},
url = {https://mlanthology.org/uai/2010/singh2010uai-bayesian/}
}