Bayesian semi non-negative matrix factorisation

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/103878

dc.contributor	Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor	Universitat Politècnica de Catalunya. SOCO - Soft Computing
dc.contributor.author	Vilamala Muñoz, Albert
dc.contributor.author	Vellido Alcacena, Alfredo
dc.contributor.author	Belanche Muñoz, Luis Antonio
dc.date	2016
dc.identifier.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.
dc.identifier.citation	978-287587027-8
dc.identifier.uri	http://hdl.handle.net/2117/103878
dc.language.iso	eng
dc.publisher	I6doc.com
dc.relation	https://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2016#ES2016-62
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject	Factorization (Mathematics) -- Computer science
dc.subject	Artificial intelligence
dc.subject	Learning systems
dc.subject	Matrix algebra
dc.subject	Neural networks
dc.subject	Bayesian
dc.subject	Bayesian frameworks
dc.subject	Mixing coefficient
dc.subject	Neuro-oncology
dc.subject	Non-negative matrix factorisation
dc.subject	Number of sources
dc.subject	Observed data
dc.subject	Source identification
dc.subject	Factorització (Matemàtica) -- Informàtica
dc.title	Bayesian semi non-negative matrix factorisation
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.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 the most adequate number of sources to describe the observed data. The proposed Bayesian Semi-NMF is preliminarily evaluated here in a real neuro-oncology problem.
dc.description.abstract	Peer Reviewed

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