Improved multi-view GEPSVM via Inter-View Difference Maximization and Intra-view Agreement Minimization. (May 2020)

• Record Type: Journal Article
• Authors: Cheng, Yawen; Yin, Hang; Ye, Qiaolin; Huang, Peng; Fu, Liyong; Yang, Zhangjing; Tian, Yuan
• Is Part Of: Neural networks. Volume 125(2020)
• Page Start: 313
• Page End: 329
• Publication Date: 2020-05
• Subjects: Multi-view learning -- GEPSVM -- L1-norm -- IMvGEPSVM -- Robustness; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2020.02.002
• Languages: English
• ISSNs: 0893-6080

Abstract

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.