Sparse generalized linear model with L(0) approximation for feature selection and prediction with big omics data

BACKGROUND: Feature selection and prediction are the most important tasks for big data mining. The common strategies for feature selection in big data mining are L (1), SCAD and MC+. However, none of the existing algorithms optimizes L (0), which penalizes the number of nonzero features directly. RE...

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Detalles Bibliográficos
Autores principales: Liu, Zhenqiu, Sun, Fengzhu, McGovern, Dermot P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Methodology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735537/
https://www.ncbi.nlm.nih.gov/pubmed/29270229
http://dx.doi.org/10.1186/s13040-017-0159-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5735537/
https://www.ncbi.nlm.nih.gov/pubmed/29270229
http://dx.doi.org/10.1186/s13040-017-0159-z

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