A Robust Supervised Variable Selection for Noisy High-Dimensional Data

The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups. Various available versions of the MRMR approach have been...

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Detalles Bibliográficos
Autores principales: Kalina, Jan, Schlenker, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4468284/
https://www.ncbi.nlm.nih.gov/pubmed/26137474
http://dx.doi.org/10.1155/2015/320385

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4468284/
https://www.ncbi.nlm.nih.gov/pubmed/26137474
http://dx.doi.org/10.1155/2015/320385

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