Locality Correlation Preserving Based One-Class Support Vector Machine

被引：0
作者：
Chang, Jian-Di [1 ]
Xing, Hong-Jie [1 ]
机构：
[1] Hebei Univ, Coll Math & Informat Sci, Key Lab Machine Learning & Computat Intelligence, Baoding 071002, Peoples R China
来源：
2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年
关键词：
Locality Correlation Preserving; One-Class Support Vector Machine; One-Class Classification; Local Geometric Structure; CLASSIFICATION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：1113 / 1118
页数：6
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