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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Adapt the mRMR Criterion for Unsupervised Feature Selection
verfasst von : Junling Xu
Erschienen in: Advanced Data Mining and Applications
Verlag: Springer Berlin Heidelberg
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Feature selection is an important task in data analysis. mRMR is an equivalent form of the maximal statistical dependency criterion based on mutual information for first-order incremental supervised feature selection. This paper presents a novel feature selection criterion which can be considered as the unsupervised version of mRMR. The concepts of relevance and redundancy are both concerned in the feature selection criterion. The effectiveness of the new unsupervised feature selection criterion is confirmed by the theoretical proof. Experimental validation is also conducted on several popular data sets, and the results show that the new criterion can select features highly correlated with the latent class variable.