基于最小流形类内离散度的支持向量机  

Support vector machine based on minimum manifold-based within-class scatter

作　　者：高艳云 庞敏[2] 

机构地区：[1]河南信息统计学院人事处,郑州450008 [2]中北大学计算机与控制工程学院,太原030051

出　　处：《计算机应用研究》2015年第9期2639-2642,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(61202311);山西省高等学校科技创新项目(2014142)

摘　　要：尽管经典分类方法支持向量机SVM在各领域广泛应用,但其在分类决策时仅关注类间间隔而忽视类内分布,因而分类能力有限。鉴于此,Zafeiriou等人提出最小类方差支持向量机MCVSVM,该方法建立在支持向量机和线性判别分析的基础上,在进行分类决策时同时考虑各类的边界信息和分布特征,因而较之SVM具有更优的泛化能力。但上述两种方法均忽略了样本的局部特征。基于上述分析,在流形判别分析的基础上提出基于最小流形类内离散度的支持向量机SVM-M2WCS。该方法在建立最优分类面时,不仅考虑各类的边界信息和分布特征,而且还保持了各类的局部流形结构。经理论分析可得该方法在一定条件下与SVM和MCVSVM等价,这表明SVM-M2WCS较之SVM和MCVSVM具有更优的泛化能力。人工数据集及标准数据集上的比较实验表明SVM-M2WCS的有效性。Support vector machine （SVM） is one of the most popular classification methods and widely used in practice. But with the development of application,it encounters a problem which seriously limits the classification efficiency：it only focuses on the margin between classes, but ignores the class distributions. In order to solve the above problem, this paper proposed min- imum class variance support vector machine （ MCVSVM ） by Zafeiriou and considered boundary information and distribution characteristics and therefore its classification efficiency was much better than SVM. The local characteristics of each class was quite important but it was regrettable that it was neglected by both SVM and MCVSVM. In view of this, this paper proposed support vector machine based on minimum manifold-based within-class scatter （SVM-MzWCS）. The theoretical and experi- mental analysis shows the effectiveness of our proposed methods.

关 键 词：支持向量机 流形判别分析 分布特征 边界信息 局部信息 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]