PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayesian non-negative matrix factorization
Mikkel N. Schmidt, Ole Winter and Lars Kai Hansen
In: Independent Component Analysis and Signal Separation, International Conference on, 2009(2009).

Abstract

Abstract: We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to approximate the posterior density of the NMF factors. On a chemical brain imaging data set, we show that this improves interpretability by providing uncertainty estimates. We discuss how the Gibbs sampler can be used for model order selection by estimating the marginal likelihood, and compare with the Bayesian information criterion. For computing the maximum a posteriori estimate we present an iterated conditional modes algorithm that rivals existing state-of-the-art NMF algorithms on an image feature extraction problem.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6524
Deposited By:Mikkel Schmidt
Deposited On:08 March 2010