Locality Correlation Preserving Based One-Class Support Vector Machine

In order to fully utilize the local geometric information of the given training set consisting of the normal data, locality correlation preserving (LCP) is introduced into the traditional one-class support vector machine (OCSVM). The proposed method, named as locality correlation preserving based one-class support vector machine (LCP-OCSVM), inherits the merits of LCP and OCSVM. It can keep locality correlation of the normal data and margin maximization between the normal data and the origin in the high-dimensional feature space. Experimental results on one synthetic data set and ten benchmark data sets demonstrate that the proposed method is superior to the traditional OCSVM and two related approaches.