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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2015-01-01
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| Series: | BioMed Research International |
| Online Access: | http://dx.doi.org/10.1155/2015/320385 |