Adapt the mRMR criterion for unsupervised feature selection

Authors:
Junling Xu
Affiliations:
School of Computer Science and Engineering, Southeast University, Nanjing, China
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Year:
2010

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Abstract

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.