Title:
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Bayesian semi non-negative matrix factorisation
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Author:
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Vilamala Muñoz, Albert; Vellido Alcacena, Alfredo; Belanche Muñoz, Luis Antonio
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
Abstract:
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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. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Factorization (Mathematics) -- Computer science -Artificial intelligence -Learning systems -Matrix algebra -Neural networks -Bayesian -Bayesian frameworks -Mixing coefficient -Neuro-oncology -Non-negative matrix factorisation -Number of sources -Observed data -Source identification -Factorització (Matemàtica) -- Informàtica |
Rights:
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Document type:
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Article - Published version Conference Object |
Published by:
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I6doc.com
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