基于最小二乘的最小类方差支撑向量机

Least-Square-based Minimum Class Variance Support Vector Machines

针对最小类方差支撑向量机(MCVSVM)在小样本情况下仅利用类内散度矩阵非零空间中信息的问题,提出基于最小二乘的最小类方差支撑向量机(LS-MCVSVM)算法,通过牛顿优化法迭代求解LS-MCVSVM的优化问题,从而有效解决了小样本问题。实验结果表明,相对于MCVSVM,LS-MCVSVM算法可进一步提高泛化能力,减少训练时间开销。

Aiming at the problem that Minimum Class Variance Support Vector Machines（MCVSVM） which utilize only information in the non-null space of the within-class scatter matrix in small sample size case, this paper presents a novel algorithm called Least-Square-based Minimum Class Variance Support Vector Machines（LS-MCVSVM）. The optimization problem of LS-MCVSVM can be solved by using Newton optimization, and the small sample problem can be avoided efficiently. Experimental results on several real datasets show that LS-MCVSVM can improve the generating ability and reduce the training time greatly.