Pairwise dependence-based unsupervised feature selection.

• We propose a new unsupervised feature selection based on information theory. • A single optimization problem is designed considering dependence among features. • We analyze the convergence of the proposed iterative algorithm. Many research topics present very high dimensional data. Because of the heavy execution times and large memory requirements, many machine learning methods have difficulty in processing these data. In this paper, we propose a new unsupervised feature selection method considering the pairwise dependence of features (feature dependency-based unsupervised feature selection, or DUFS). To avoid selecting redundant features, the proposed method calculates the dependence among features and applies this information to a regression-based unsupervised feature selection process. We can select small feature set with the dependence among features by eliminating redundant features. To consider the dependence among features, we used mutual information widely used in supervised feature selection area. To our best knowledge, it is the first study to consider the pairwise dependence of features in the unsupervised feature selection method. Experimental results for six data sets demonstrate that the proposed method outperforms existing state-of-the-art unsupervised feature selection methods in most cases. [ABSTRACT FROM AUTHOR]