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 hyperplane. 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.