最小类方差支持向量机与零空间分类器的集成

最小类方差支持向量机(MCVSVM)充分考虑数据的分布信息，但是在小样本情况下却仅利用类内散度矩阵非零空间中的信息。为了综合利用类内散度矩阵非零空间和零空间中的信息来进一步提高分类性能，文中首先在零空间中建立一种分类器——零空间分类器(NSC)，然后再把MCVSVM和NSC进行融合，从而进一步提出集成分类器(EC)。不同于MCVSVM和NSC，EC综合考虑非零空间和零空间中的信息，体现出更强的泛化能力。最后通过实验验证算法的有效性。

Ensemble Classifier Based on Minimum Class Variance SVM and Null Space Classifier

The Minimum Class Variance Support Vector Machine (MCVSVM) takes into consideration both the samples in the boundaries and the distribution of the classes. However, only the information in the non-null space of the within-class scatter matrix is utilized in the case of small sample size. To further improve the classification performance, in this paper the Null Space Classifier (NSC) which is rooted in the null space is first presented, then an Ensemble Classifier (EC) is proposed by fusing the MCVSVM and the NSC. Different form the MCVSVM and the NSC, the EC considers the information both in the non-null space and in the null space and has better generalizability. Finally, experimental results on several real datasets indicate the effectiveness of the EC.