Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation
Abstract
In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian inference, and a maximum-a-posteriori approach. The variational approach is new for the Bayesian nonnegative models. We compare their convergence, and robustness to noise and sparsity of the data, on both synthetic and real-world datasets. Furthermore, we extend the models with the Bayesian automatic relevance determination prior, allowing the models to perform automatic model selection, and demonstrate its efficiency.
Cite
@article{arxiv.1707.05147,
title = {Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation},
author = {Thomas Brouwer and Jes Frellsen and Pietro Lió},
journal= {arXiv preprint arXiv:1707.05147},
year = {2017}
}
Comments
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017). The final publication will be available at link.springer.com. arXiv admin note: text overlap with arXiv:1610.08127