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Multiview Generalized Eigenvalue Proximal Support Vector Machines (MvGSVMs) is an effective multi-view classification algorithm, which effectively combines multi-view learning and classification. Then it was found that in the classification learning task, the classifier combined with multi-view learning has a better classification effect than considering only a single view. In order to utilize the multi-view learning framework more fully and accurately, we further research this. We explore the internal relationship between different views between samples to replace the method of connecting different views through distance combinations. We propose a new method named Multi-view Generalized Support Vector Machine via Mining the Inherent Relationship between Views (MRMvGSVM). At the same time, we use the L2,1-norm constraint relationship matrix as a multi-view regularization term to select the most relevant sample data from different views. It not only helps to improve the accuracy of classification but also reduces the influence of extraneous factors to a certain extent and improves the robustness of the algorithm. The effectiveness of the algorithm is proved by theory and experiments on UCI, and Face and Fire Smoke image datasets. [ABSTRACT FROM AUTHOR]

Discriminative Feature Selection (DFS) is an algorithm, proposed recently for effective feature selection by considering both joint linear discriminant analysis and row sparsity regularization. However, this method is not robust enough to protect the data from outliers, because it utilizes the squared L 2 -norm distance metric. To overcome this problem, we present in this paper, a novel discriminative feature selection algorithm, which uses the robust L 2 , 1 -norm for measuring the distances in DFS. Although the algorithm is apparently simple, it should not be considered trivial because of its non-convexity. Also, we present an analysis of the convergence, both theoretical and empirical. More importantly, we proposed an iterative algorithm to achieve optimal results. Experimental results, using various data sets, demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]