Improved multi-view GEPSVM via Inter-View Difference Maximization and Intra-view Agreement Minimization

Yawen Cheng; Hang Yin; Qiaolin Ye; Peng Huang; Liyong Fu; Zhangjing Yang; Yuan Tian

doi:10.1016/j.neunet.2020.02.002

Improved multi-view GEPSVM via Inter-View Difference Maximization and Intra-view Agreement Minimization

Neural Netw. 2020 May:125:313-329. doi: 10.1016/j.neunet.2020.02.002. Epub 2020 Feb 20.

Authors

Yawen Cheng¹, Hang Yin², Qiaolin Ye², Peng Huang², Liyong Fu³, Zhangjing Yang⁴, Yuan Tian⁵

Affiliations

¹ College of Information Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu, 210037, China; Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China.
² College of Information Science and Technology, Nanjing Forestry University, Nanjing, Jiangsu, 210037, China.
³ Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; Key Laboratory of Forest Management and Growth Modeling, National Forestry and Grassland Administration, Beijing 100091, China. Electronic address: fuliyong840909@163.com.
⁴ Jiangsu Key Laboratory of Auditing Information Engineering, School of Information Engineering, Nanjing Audit University, Nanjing, Jiangsu 211815, China.
⁵ School of Computer Engineering, Nanjing Institute of Technology, West Beijing Road, Nanjing, Jiangsu, China.

PMID: 32172141
DOI: 10.1016/j.neunet.2020.02.002

Abstract

Multiview Generalized Eigenvalue Proximal Support Vector Machine (MvGEPSVM) is an effective method for multiview data classification proposed recently. However, it ignores discriminations between different views and the agreement of the same view. Moreover, there is no robustness guarantee. In this paper, we propose an improved multiview GEPSVM (IMvGEPSVM) method, which adds a multi-view regularization that can connect different views of the same class and simultaneously considers the maximization of the samples from different classes in heterogeneous views for promoting discriminations. This makes the classification more effective. In addition, L1-norm rather than squared L2-norm is employed to calculate the distances from each of the sample points to the hyperplane so as to reduce the effect of outliers in the proposed model. To solve the resulting objective, an efficient iterative algorithm is presented. Theoretically, we conduct the proof of the algorithm's convergence. Experimental results show the effectiveness of the proposed method.

Keywords: GEPSVM; IMvGEPSVM; L1-norm; Multi-view learning; Robustness.

MeSH terms

Pattern Recognition, Automated / methods
Support Vector Machine / standards*