Show simple item record

dc.contributor.authorVilamala Muñoz, Albert
dc.contributor.authorVellido Alcacena, Alfredo
dc.contributor.authorBelanche Muñoz, Luis Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2017-05-02T08:11:19Z
dc.date.available2017-05-02T08:11:19Z
dc.date.issued2016
dc.identifier.citationVilamala, 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.isbn978-287587027-8
dc.identifier.urihttp://hdl.handle.net/2117/103878
dc.description.abstractNon-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.format.extent6 p.
dc.language.isoeng
dc.publisherI6doc.com
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshFactorization (Mathematics) -- Computer science
dc.subject.otherArtificial intelligence
dc.subject.otherLearning systems
dc.subject.otherMatrix algebra
dc.subject.otherNeural networks
dc.subject.otherBayesian
dc.subject.otherBayesian frameworks
dc.subject.otherMixing coefficient
dc.subject.otherNeuro-oncology
dc.subject.otherNon-negative matrix factorisation
dc.subject.otherNumber of sources
dc.subject.otherObserved data
dc.subject.otherSource identification
dc.titleBayesian semi non-negative matrix factorisation
dc.typeConference report
dc.subject.lemacFactorització (Matemàtica) -- Informàtica
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2016#ES2016-62
dc.rights.accessOpen Access
local.identifier.drac19287376
dc.description.versionPostprint (published version)
local.citation.authorVilamala, A.; Vellido, A.; Belanche, Ll.
local.citation.contributorEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
local.citation.pubplaceBruges
local.citation.publicationNameESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016
local.citation.startingPage195
local.citation.endingPage200


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder