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Bayesian semi non-negative matrix factorisation

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Vilamala Muñoz, Albert
Vellido Alcacena, AlfredoMés informacióMés informacióMés informació
Belanche Muñoz, Luis AntonioMés informacióMés informacióMés informació
Document typeConference report
Defense date2016
PublisherI6doc.com
Rights accessOpen Access
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
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.
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. 
URIhttp://hdl.handle.net/2117/103878
ISBN978-287587027-8
Publisher versionhttps://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2016#ES2016-62
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  • Departament de Ciències de la Computació - Ponències/Comunicacions de congressos [1.191]
  • SOCO - Soft Computing - Ponències/Comunicacions de congressos [109]
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