One-class classification using a support vector machine with a quasi-linear kernel

This article proposes a novel method for one-class classification based on a divide-and-conquer strategy to improve the one-class support vector machine (SVM). The idea is to build a piecewise linear separation boundary in the feature space to separate the data points from the origin, which is expected to have a more compact region in the input space. For the purpose, the input space of the dataset is first divided into a group of partitions by using a partitioning mechanism of top s% winner-take-all autoencoder. A gated linear network is designed to implement a group of linear classifiers for each partition, in which the gate signals are generated from the autoencoder. By applying a one-class SVM (OCSVM) formulation to optimize the parameter set of the gated linear network, the one-class classifier is implemented in an exactly same way as a standard OCSVM with a quasi-linear kernel composed using a base kernel with the gate signals. The proposed one-class classification method is applied to different real-world datasets, and simulation results show that it shows a better performance than a traditional OCSVM.