Multi-view learning based on maximum margin of twin spheres support vector machine

被引:6
|
作者
Wang, Huiru [1 ]
Zhou, Zhijian [2 ]
机构
[1] Beijing Forestry Univ, Coll Sci, Beijing, Peoples R China
[2] China Agr Univ, Coll Sci, 17 Qinghua East Rd, Beijing 100083, Peoples R China
基金
中国国家自然科学基金;
关键词
Multi-view learning; twin spheres; SVM; maximum margin; consensus principle;
D O I
10.3233/JIFS-202427
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Multi-view learning utilizes information from multiple representations to advance the performance of categorization. Most of the multi-view learning algorithms based on support vector machines seek the separating hyperplanes in different feature spaces, which may be unreasonable in practical application. Besides, most of them are designed to balanced data, which may lead to poor performance. In this work, a novel multi-view learning algorithm based on maximum margin of twin spheres support vector machine (MvMMTSSVM) is introduced. The proposed method follows both maximum margin principle and consensus principle. By following the maximum margin principle, it constructs two homocentric spheres and tries to maximize the margin between the two spheres for each view separately. To realize the consensus principle, the consistency constraints of two views are introduced in the constraint conditions. Therefore, it not only deals with multi-view class-imbalanced data effectively, but also has fast calculation efficiency. To verify the validity and rationlity of our MvMMTSSVM, we do the experiments on 24 binary datasets. Furthermore, we use Friedman test to verify the effectiveness of MvMMTSSVM.
引用
收藏
页码:11273 / 11286
页数:14
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