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dc.contributor.authorLaurberg, Hen_US
dc.contributor.authorChristensen, MGen_US
dc.contributor.authorPlumbley, MDen_US
dc.contributor.authorHansen, LKen_US
dc.contributor.authorJensen, SHen_US
dc.date.accessioned2011-05-26T15:10:41Z
dc.date.available2008-03-13en_US
dc.date.issued2008en_US
dc.identifier.issn1687-5265en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/1095
dc.description.abstractWe investigate the conditions for which nonnegative matrix factorization (NMF) is unique and introduce several theorems which can determine whether the decomposition is in fact unique or not. The theorems are illustrated by several examples showing the use of the theorems and their limitations. We have shown that corruption of a unique NMF matrix by additive noise leads to a noisy estimation of the noise-free unique solution. Finally, we use a stochastic view of NMF to analyze which characterization of the underlying model will result in an NMF with small estimation errors.en_US
dc.format.extent764206 - ?en_US
dc.languageengen_US
dc.relation.ispartofComput Intell Neuroscien_US
dc.titleTheorems on positive data: on the uniqueness of NMF.en_US
dc.typeArticle
dc.identifier.doi10.1155/2008/764206en_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/18497868en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
dcterms.dateAccepted2008-03-13en_US


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