Abstract：Aiming at some potential correlation between features in feature space, spectral clustering and neighborhood mutual information are exploited to explore the correlation features and obtain maximal relevant feature subset, respectively. And a feature selection algorithm combining spectral clustering and neighborhood mutual information is proposed. In this paper, the neighborhood mutual information is firstly applied to remove uncorrelated features, and then the spectral clustering is utilized to group features. The features of the same group are strongly correlated and the features of different groups are strongly different. Then, the feature subset strongly associated with class label is selected from each feature group. Finally, all selected feature subsets are collected together to form the final selected features. Extensive experiment is conducted with two different classifiers. Experimental results show that the proposed model effectively improves the classification performance with less features.
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