A survey of feature selection algorithms based on graph
特征选择作为数据处理的预步骤成为近年来的研究热点.借鉴图的方法,可认为重要的特征应该具有使同类样本更加聚集在同类之中,而使非同类样本间的间隔应该尽可能大的特点.首先详细介绍了当前常用的基于图的特征选择算法,并对其进行了分类比较;接着给出了当前基于图的特征选择算法存在的问题;最后指出了基于图的特征选择算法的研究趋势.Feature selection as the pre-step in the data processing has become a hot topic in recent years. Using the methods of graphs, it can be considered that the important features should be more aggregated in the same class, and more dispersed in the different class. This paper firstly introduced classification and compared the current familiar feature selection algorithms based on graph. Secondly, it pointed out the existing problems of feature selection algorithms based on graph. Finally, this paper involved what calls for future study.