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

作     者：Liang, Peifeng Li, Weite Tian, Hao Hu, Jinglu 

作者机构：Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka 8080135 Japan Hubei Univ Econ Sch Informat Engn 8 Yangqiaohu Ave Wuhan 430205 Hubei Peoples R China 

出 版 物：《IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING》 (日本电气工程师学会电气与电子工程汇刊)

年 卷 期：2019年第14卷第3期

页      面：449-456页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：Science Research Key Project of the Department of Education in Hubei Province  China [D20162202] 

主　　题：one-class classification support vector machine winner-take-all autoencoder kernel composition 

摘      要：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. (c) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.