Abstract: 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.

Key words: feature selection, Linear Discriminant Analysis (LDA), categorization

摘要： 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UCI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。

关键词: 特征选择, 线性判别分析, 分类