Bayesian Inference for Nonnegative Matrix Factorisation Models
Author
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2009, Issue 2009 (31 Dec. 2009), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-05-27
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
We describe nonnegative matrix factorisation (NMF) with a Kullback-Leibler (KL) error measure in a statistical framework, with a hierarchical generative model consisting of an observation and a prior component.
Omitting the prior leads to the standard KL-NMF algorithms as special cases, where maximum likelihood parameter estimation is carried out via the Expectation-Maximisation (EM) algorithm.
Starting from this view, we develop full Bayesian inference via variational Bayes or Monte Carlo.
Our construction retains conjugacy and enables us to develop more powerful models while retaining attractive features of standard NMF such as monotonic convergence and easy implementation.
We illustrate our approach on model order selection and image reconstruction.
American Psychological Association (APA)
Cemgil, Ali Taylan. 2009. Bayesian Inference for Nonnegative Matrix Factorisation Models. Computational Intelligence and Neuroscience،Vol. 2009, no. 2009, pp.1-17.
https://search.emarefa.net/detail/BIM-497893
Modern Language Association (MLA)
Cemgil, Ali Taylan. Bayesian Inference for Nonnegative Matrix Factorisation Models. Computational Intelligence and Neuroscience No. 2009 (2009), pp.1-17.
https://search.emarefa.net/detail/BIM-497893
American Medical Association (AMA)
Cemgil, Ali Taylan. Bayesian Inference for Nonnegative Matrix Factorisation Models. Computational Intelligence and Neuroscience. 2009. Vol. 2009, no. 2009, pp.1-17.
https://search.emarefa.net/detail/BIM-497893
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-497893