中山大学学报（自然科学版）2009，Vol.48Issue(6)：10-17,8.
嵌入数据结构信息的单类支持向量机及其线性规划算法
Embedding Target Data's Structural Distribution Information into One-Class SVM and Its Linear Programming Algorithm
摘要
Abstract
In order to distinguish the target class from outliers accurately, One-Class Classifier ( OCC) should take into account the prior knowledge of the target class. However, One-Class SVM ( OCSVM), the state-of-the-art OCC, neglects the data's distribution information while finding the optimal hyper-plane. Structured OCSVM (SOCSVM) , the novel proposed OCC, alleviates this problem by embedding the within-class scattered matrix of the target data into OCSVM. As a result, SOCSVM not only overcomes the above disadvantage of the OCSVM, but also provides a unified framework for the present SVM algorithms how to consider intrinsic structure of the data. Moreover, to improve the efficiency of SOCSVM , linear programming algorithm called SlpOCSVM is proposed to instead of the quadratic programming solving for SOCSVM. Through minimizing the functional distance of the data's mean to the hyperplane, the optimal hyperplane is attracted automatically to the place of the minimum positive half space without borrowing the origin as a representative of the outlier anymore . The experiment results on toy problem and real data sets demonstrate the advantage of SOCSVM and its linear programming algorithm.关键词
单类分类器/支持向量机/结构信息/二次规划/线性规划Key words
one-class classifier/ support vector machine/ structured information/ quadratic programming/ linear programming分类
信息技术与安全科学引用本文复制引用
冯爱民,刘学军,孙廷凯..嵌入数据结构信息的单类支持向量机及其线性规划算法[J].中山大学学报（自然科学版）,2009,48(6):10-17,8.基金项目
国家自然科学基金资助项目(60603029,60703016,60803049) （60603029,60703016,60803049）
