A Bayesian Matrix Factorization Model for Relational Data
This work addresses the challenge of enhancing predictive models in neuroscience by integrating correlated data sources, though it appears incremental as it builds on existing matrix factorization and Bayesian methods.
The authors tackled the problem of improving predictive accuracy in relational learning by framing it as a matrix factorization problem and proposing a hierarchical Bayesian model, resulting in a demonstrated improvement in predicting brain response to stimuli when augmented with side information.
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.