The recent literature indicates that the global structure preservation is very important for sparse representation based supervised feature selection. However, the selected features in preserving different global structures are often different and which global structure is the best not yet known. As a result, which feature selection result we should trust is confusing. The reason may be that each global structure does not carry enough information for the data, as the distribution of a real life data is very complex. To overcome the above problem, in this paper, a dual global structure preservation based supervised feature selection (DGSPSFS) method is proposed. In DGSPSFS, the supervised dimensional reduction method based on manifold learning is used to calculate the response matrix, which can contain more information of the data. And a new sparse representation framework that can preserve two global structures in the same time is proposed, which can comprehensively use two response matrices to fully utilize the information of the data. As a result, the features that can carry more information are selected. A comprehensive experimental study is then conducted in order to compare our feature selection algorithms with many state-of-the art ones in supervised learning scenarios. The conducted experiments validate the effectiveness of our feature selection.

中文翻译：

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基于双重全局结构保存的监督特征选择

最近的文献表明，全局结构的保存对于基于稀疏表示的监督特征选择非常重要。但是，在保留不同的全局结构时所选择的特征通常是不同的，哪种全局结构是目前尚不清楚的。结果，我们应该信任的特征选择结果令人困惑。原因可能是每个现实结构都没有为数据携带足够的信息，因为现实生活中的数据分布非常复杂。为了克服上述问题，本文提出了一种基于双重全局结构保存的监督特征选择（DGSPSFS）方法。在DGSPSFS中，基于流形学习的监督降维方法用于计算响应矩阵，该矩阵可以包含更多数据信息。提出了一种可以同时保留两个全局结构的稀疏表示框架，该框架可以综合利用两个响应矩阵来充分利用数据信息。结果，选择了可以承载更多信息的功能。然后进行了全面的实验研究，以便在监督学习场景中将我们的特征选择算法与许多最新技术进行比较。进行的实验验证了我们选择特征的有效性。然后进行了全面的实验研究，以便在监督学习场景中将我们的特征选择算法与许多最新技术进行比较。进行的实验验证了我们选择特征的有效性。然后进行了全面的实验研究，以便在监督学习场景中将我们的特征选择算法与许多最新技术进行比较。进行的实验验证了我们选择特征的有效性。