基于线性判别分析的特征选择

Feature selection based on linear discriminant analysis

提出一种新颖的基于特征抽取的特征选择方法,将特征选择问题建模为在子空间中的搜索问题,采用线形判别分析(LDA)的投影思想,对LDA施加一定的限制将其转换为对子空间的搜索优化问题,从而通过解LDA的优化问题得到特征选择的解,进一步把特征选择问题推导简化为对特征的评分和排序过程。通过在UC I机器学习库和Reuters-21578文本数据集上的实验,验证了该方法以较少的特征获得了比全部特征更好的分类结果。

The paper proposed a new approach of feature selection based on Constrained Linear Discriminant Analysis （CLDA）, which modeled feature selection as a search problem in subspace and made optimal solution subject to some restrictions. Furthermore, CLDA optimization problem was transformed into a process of scoring and sorting features. Experiments on UCI machine learning repository and Reuters-21578 dataset show that the proposed approach can consistently obtain better results with fewer features than that with all features.