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Título:
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Bayesian semi non-negative matrix factorisation
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Autor/a:
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Vilamala Muñoz, Albert; Vellido Alcacena, Alfredo; Belanche Muñoz, Luis Antonio
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
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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. |
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Abstract:
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Peer Reviewed |
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Materia(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 |
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Derechos:
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Tipo de documento:
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Artículo - Versión publicada
Objeto de conferencia |
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Editor:
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I6doc.com
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