Schachtner, R. and Poeppel, G. and Tome, A. M. and Puntonet, C. G. and Lang, E. W. (2014) A new Bayesian approach to nonnegative matrix factorization: Uniqueness and model order selection. NEUROCOMPUTING, 138. pp. 142-156. ISSN 0925-2312, 1872-8286
Full text not available from this repository. (Request a copy)Abstract
NMF is a blind source separation technique decomposing multivariate non-negative data sets into meaningful non-negative basis components and non-negative weights. There are still open problems to be solved: uniqueness and model order selection as well as developing efficient NMF algorithms for large scale problems. Addressing uniqueness issues, we propose a Bayesian optimality criterion (BOC) for NMF solutions which can be derived in the absence of prior knowledge. Furthermore, we present a new Variational Bayes NMF algorithm VBNMF which is a straight forward generalization of the canonical Lee-Seung method for the Euclidean NMF problem and demonstrate its ability to automatically detect the actual number of components in non-negative data. (C) 2014 Elsevier B.V. All rights reserved.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | INDEPENDENT COMPONENT ANALYSIS; BLIND SOURCE SEPARATION; CONSTITUENT SPECTRA; GENETIC ALGORITHMS; CLASSIFICATION; RECOVERY; VALUES; PCA; Bayes NMF; Variational Bayes; Bayesian optimality criterion; Generalized Lee-Seung update rules |
| Subjects: | 500 Science > 570 Life sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 30 Aug 2019 12:03 |
| Last Modified: | 30 Aug 2019 12:03 |
| URI: | https://pred.uni-regensburg.de/id/eprint/9729 |
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