Dynamic graph fusion label propagation for semi-supervised multi-modality classification

The relevance modeling between multi-graph and label describes their interrelation.The nonlinear relation of multi-graph can approximate to the data manifold structure.Multi-graph fusion integrates into label propagation for classification performance. The key of label propagation heavily depends on how to capture the manifold structure of the data, which usually is represented by the graph. In the semi-supervised multi-modality classification, exiting methods often optimize the linear relation of multi-graph for label propagation. However, the intrinsic manifold structure is not completely revealed by the linear fusion of multi-graph because the label changes in each iterating propagation dynamically influence the fusion relation of multi-graph. In other words, the fusion relation of multi-graph should be nonlinear because of the label changes in the propagation process, and can not be precisely described by the fixed linear relation in existing methods. To evaluate this nonlinear relationship influence on the classification performance of label propagation, we propose dynamic graph fusion label propagation (DGFLP) for the semi-supervised multi-modality classification. DGFLP is able to jointly consider the relation of multi-graph and the unique distribution of each graph, and models the various relevance of multi-graph in the propagation process. Moreover, the DGFLP alternately integrates the tradition label propagation and the new model function to describe the interaction between the multi-graph and label. The DGFLP solution provides not only the classification label but also the nonlinear relation that encodes the dynamical multi-graph relationship changes in label propagation. The experimental results demonstrate that DGFLP outperforms state-of-art methods on the ORL, AR, scenes 15, Caltech 101, and Caltech 256 databases. Display Omitted