Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

Thomas Brouwer, Jes Frellsen, Pietro Liò

Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review

Abstrakt

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.
OriginalsprogEngelsk
TitelThe European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database 2017
ForlagSpringer
Publikationsdato2017
Sider513-529
ISBN (Trykt)Print ISBN 978-3-319-71248-2
ISBN (Elektronisk)978-3-319-71249-9
DOI
StatusUdgivet - 2017
NavnLecture Notes in Computer Science
Vol/bind10534
ISSN0302-9743

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