基于随机子空间-正交局部保持投影的支持向量机  

Support Vector Machine Based on Random Subspace and Orthogonal Locality Preserving Projection

作　　者：王雪松[1] 高阳[1] 程玉虎[1] 

机构地区：[1]中国矿业大学信息与电气工程学院,江苏徐州221116

出　　处：《电子学报》2011年第8期1746-1750,共5页Acta Electronica Sinica

基　　金：国家自然科学基金(No.60804022;No.60974050;No.61072094);教育部新世纪优秀人才支持计划(No.NCET-08-0836);霍英东教育基金会青年教师基金(No.121066);江苏省自然科学基金(No.BK2008126)

摘　　要：针对高维数、小样本数据分类问题,提出一种基于随机子空间-正交局部保持投影的支持向量机.利用随机子空间方法对原始高维样本的特征空间进行多次随机采样,生成多个具有不同特征子集的基支持向量机(SVM)分类器;利用正交局部保持投影对各基SVM分类器的样本进行特征提取,实现维数约简;然后,利用降维后的样本对各基SVM分类器进行训练;采用贝叶斯求和准则将各基SVM的分类结果进行融合以得到最终的分类结果.典型人脸数据库识别结果验证了本方法的可行性和有效性.In order to deal with the classification problem for high-dimensional and small-sized data,a kind of support vector machine based on random subspace and orthogonal locality preserving projection was proposed.The random subspace method was used to select a feature subset from the original feature space randomly for several times.Based on the selected feature subset,several base support vector machine（SVM） classifiers were generated.The orthogonal locality preserving projection method was adopted to carry out feature extraction on the samples of each base classifiers,which can,effectively,realize dimensionality reduction.We applied the processed samples to train each base classifiers.The results of the base SVM classifiers were integrated to obtain the final classification result,using a bayesian sum rule.Results on two publicly available face databases show the feasibility and validity of our proposed method.

关 键 词：随机子空间 正交局部保持投影 支持向量机 特征提取 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]