当前位置: X-MOL 学术Neural Netw. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Improved multi-view GEPSVM via Inter-View Difference Maximization and Intra-view Agreement Minimization.
Neural Networks ( IF 7.8 ) Pub Date : 2020-02-20 , DOI: 10.1016/j.neunet.2020.02.002
Yawen Cheng 1 , Hang Yin 2 , Qiaolin Ye 2 , Peng Huang 2 , Liyong Fu 3 , Zhangjing Yang 4 , Yuan Tian 5
Affiliation  

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.



中文翻译:

通过视图间差异最大化和视图内协定最小化改进的多视图GEPSVM。

多视图广义特征值近端支持向量机(MvGEPSVM)是最近提出的一种有效的多视图数据分类方法。但是,它忽略了不同观点之间的区别以及同一观点的认同。而且,没有鲁棒性保证。在本文中,我们提出了一种改进的多视图GEPSVM(IMvGEPSVM)方法,该方法增加了一个多视图正则化方法,该规则化方法可以连接相同类别的不同视图,同时考虑在异构视图中最大化来自不同类别的样本以促进区分。这使分类更加有效。另外,采用L1范数而不是平方L2范数来计算从每个样本点到超平面的距离,以减少所提出模型中离群值的影响。为了解决最终目标,提出了一种有效的迭代算法。从理论上讲,我们进行算法收敛性的证明。实验结果表明了该方法的有效性。

更新日期:2020-02-20
down
wechat
bug