Loading...
Loading...

Go to the content (press return)

Bayesian semi non-negative matrix factorisation

Author
Vilamala, A.; Vellido, A.; Belanche, Ll.
Type of activity
Presentation of work at congresses
Name of edition
24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Date of publication
2016
Presentation's date
2016-04
Book of congress proceedings
ESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016
First page
195
Last page
200
Publisher
I6doc.com
Repository
http://hdl.handle.net/2117/103878 Open in new window
URL
https://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2016#ES2016-62 Open in new window
Abstract
Non-negative Matrix Factorisation (NMF) has become a standard method for source identification when data, sources and mixing coefficients are constrained to be positive-valued. The method has recently been extended to allow for negative-valued data and sources in the form of Semi-and Convex-NMF. In this paper, we re-elaborate Semi-NMF within a full Bayesian framework. This provides solid foundations for parameter estimation and, importantly, a principled method to address the problem of choosing...
Citation
Vilamala, A., Vellido, A., Belanche, L. Bayesian semi non-negative matrix factorisation. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "ESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016". Bruges: I6doc.com, 2016, p. 195-200.
Keywords
Artificial intelligence, Bayesian, Bayesian frameworks, Learning systems, Matrix algebra, Mixing coefficient, Neural networks, Neuro-oncology, Non-negative matrix factorisation, Number of sources, Observed data, Source identification
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

Participants

Attachments