Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

Thomas Brouwer, Jes Frellsen, Pietro Liò

ECML-PKDD 2017 pp. 513-529

doi:10.1007/978-3-319-71249-9_31 /ecmlpkdd/2017/brouwer2017ecmlpkdd-comparative/

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. Code and data related to this chapter are availabe at: https://github.com/ThomasBrouwer/BNMTF_ARD.

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Text

Brouwer et al. "Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71249-9_31

Markdown

[Brouwer et al. "Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/brouwer2017ecmlpkdd-comparative/) doi:10.1007/978-3-319-71249-9_31

BibTeX

@inproceedings{brouwer2017ecmlpkdd-comparative,
  title     = {{Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation}},
  author    = {Brouwer, Thomas and Frellsen, Jes and Liò, Pietro},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2017},
  pages     = {513-529},
  doi       = {10.1007/978-3-319-71249-9_31},
  url       = {https://mlanthology.org/ecmlpkdd/2017/brouwer2017ecmlpkdd-comparative/}
}