摘要
支持向量机(SVM)作为正则化方法的一个特例在模式识别领域得到了成功地运用,然而传统的SVM方法作为一种有监督的学习方法主要依据最大间隔原则得到决策超平面的法向量,而并没有充分考虑样本内在的几何结构以及所蕴含的判别信息.因此,本文将线性判别分析(LDA)的类内散度和保局投影(LPP)的基本原理引入到SVM中,提出基于全局和局部保持的半监督支持向量机:GLSSVM,该方法在继承传统的SVM方法的特点的基础上,充分考虑样本间具有的全局和局部几何结构,体现样本间所蕴含的局部和全局判别信息,同时满足作为半监督方法的必须依据的一致性假设,从而在一定程度上提高了分类精度.通过在人造数据集和真实数据集上的测试表明该方法具有上述优势.
The support vector machine（SVM）,as one of special regularization methods,has been used successfully in the field of pattern recognition.However,the traditional SVM,a supervised learning method,gets the normal vector of the decision boundary mainly according to the largest interval principle but has not considered the underlying geometric structure and the discriminant information fully.Therefore,a global and local preserving based semi-supervised support vector machine：GLSSVM,is presented in this paper by introducing the basic theories of the locality preserving projections（LPP） and the within-class scatter of linear discriminant analysis（LDA） into the SVM.This method inherits the characteristics of the traditional SVM,fully considers the global and local geometric structure between samples,shows the global and local underlying discriminant information and meets the consistency assumption which the semi-supervised method must coincides with so that the shortcomings of the supervised methods can be overcome and the classification accuracy can be increased.The tests on the artificial and real datasets show the above mentioned advantages of the GLSSVM method.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2010年第7期1626-1633,共8页
Acta Electronica Sinica
基金
国家863高技术研究发展计划(No.2007AA1Z158
No.2006AA10Z313)
国家自然科学基金(No.60903100
No.90820002)
国防应用基础研究基金(No.A1420461266)
江苏省普通高校研究生科研创新计划(No.CX09B-175Z)
浙江大学CAD&CG国家重点实验室开放课题(No.A0802)
关键词
支持向量机
保局投影
线性判别分析
半监督
一致性假设
support vector machines
locality preserving projection
linear discriminant analysis
semi-supervised
consistency assumption
