Abstract 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.

中文翻译：

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基于成对依赖的无监督特征选择

摘要 许多研究课题都呈现非常高维的数据。由于大量的执行时间和大量的内存需求，许多机器学习方法在处理这些数据方面存在困难。在本文中，我们提出了一种新的无监督特征选择方法，考虑到特征的成对依赖关系（基于特征依赖的无监督特征选择，或 DUFS）。为了避免选择冗余特征，所提出的方法计算特征之间的依赖关系并将此信息应用于基于回归的无监督特征选择过程。我们可以通过消除冗余特征来选择具有特征之间依赖性的小特征集。为了考虑特征之间的依赖性，我们使用了在监督特征选择领域广泛使用的互信息。据我们所知，这是第一个在无监督特征选择方法中考虑特征的成对依赖性的研究。六个数据集的实验结果表明，在大多数情况下，所提出的方法优于现有的最先进的无监督特征选择方法。