Non-negative matrix factorization by maximizing correntropy for cancer clustering

BACKGROUND: Non-negative matrix factorization (NMF) has been shown to be a powerful tool for clustering gene expression data, which are widely used to classify cancers. NMF aims to find two non-negative matrices whose product closely approximates the original matrix. Traditional NMF methods minimize...

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Detalles Bibliográficos
Autores principales: Wang, Jim Jing-Yan, Wang, Xiaolei, Gao, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3659102/
https://www.ncbi.nlm.nih.gov/pubmed/23522344
http://dx.doi.org/10.1186/1471-2105-14-107

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3659102/
https://www.ncbi.nlm.nih.gov/pubmed/23522344
http://dx.doi.org/10.1186/1471-2105-14-107

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