A Bregman-proximal point algorithm for robust non-negative matrix factorization with possible missing values and outliers - application to gene expression analysis

BACKGROUND: Non-Negative Matrix factorization has become an essential tool for feature extraction in a wide spectrum of applications. In the present work, our objective is to extend the applicability of the method to the case of missing and/or corrupted data due to outliers. RESULTS: An essential pr...

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Detalles Bibliográficos
Autores principales: Chrétien, Stéphane, Guyeux, Christophe, Conesa, Bastien, Delage-Mouroux, Régis, Jouvenot, Michèle, Huetz, Philippe, Descôtes, Françoise
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009666/
https://www.ncbi.nlm.nih.gov/pubmed/27585655
http://dx.doi.org/10.1186/s12859-016-1120-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009666/
https://www.ncbi.nlm.nih.gov/pubmed/27585655
http://dx.doi.org/10.1186/s12859-016-1120-8

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