Learning Multi-Graph Regularization for SVM Classification
Vasileios Mygdalis, Anastasios Tefas, Ioannis Pitas · 2018
A classification method that emphasizes on learning the hyperplane that separates the training data with the maximum margin in a regularized space, is presented. In the proposed method, this regularized space is derived by exploiting multiple graph structures, in the SVM optimization process. Each of the employed graph structure carries some information concerning a geometric or semantic property about the training data, e.g., local neighborhood area and global geometric data relationships. The proposed method introduces information from each graph type to the standard SVM objective, as a projection of the SVM hyperplane to such a direction, where a specific property of the training data is highlighted. We show that each data property can be encoded in a regularized kernel matrix. Finally, response in the optimal classification space can be obtained by exploiting a weighted combination of multiple regularized kernel matrices. Experimental results in face recognition and object classification denote the effectiveness of the proposed method.