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中文摘要：
在数据分析中，特征选择可以用来降低特征的冗余，提高分析结果的可理解性和发现高维数据中隐藏的结构．提出了一种基于互信息的无监督的特征选择方法（UFS-MI），在UFS—MI中，使用了一种综合考虑了相关度和冗余度的特征选择标准UmRMR（无监督最小冗余最大相关）来评价特征的重要性．相关度和冗余度分别使用互信息来度量特征与潜在类别变量之间的依赖和特征与特征之间的依赖．UFS—MI同时适用于数值型和非数值型特征．在理论上证明了UFS—MI的有效性，实验结果也表明UFS—MI可以达到与传统的特征选择方法相当甚至更好的性能．
英文摘要：
In data analysis, feature selection can be used to reduce the redundancy of features, improve the comprehensibility of models, and identify the hidden structures in high-dimensional data. In this paper, we propose a novel unsupervised feature selection approach based on mutual information called UFS-MI. In UFS-MI, we use a feature selection criterion, UrnRMR, to evaluate the importance of each feature, which takes into account both relevance and redundancy. The relevance and redundancy respectively use mutual information to measure the dependence of features on the latent class and the dependence between features. In the new algorithm, features are selected or ranked in a stepwise way, one at a time, by estimating the capability of each specified candidate feature to decrease the uncertainty of other features （i. e. the capability of retaining the information contained in other features）. The effectiveness of UFS-MI is confirmed by the theoretical proof which shows it can select features highly correlated with the latent class. An empirical comparison between UFS-MI and several traditional feature selection methods are also conducted on some popular data sets and the results show that UFS-MI can attain better or comparable performance and it is applicable to both numerical and non-numerical features.
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