Multi-graph embedding discriminative correlation feature learning for image recognition

Most of existing graph-based correlation analysis algorithms construct one graph for one view. However, only one graph is difficult to well reveal intrinsic geometry structure of the view. In this paper, we construct multiple graphs for each view by means of a dimensionality partitioning method, and then propose a novel multi-graph embedding discriminative correlation feature learning algorithm that focuses on multiple graph learning and label-based discriminating enhancement under the multi-view correlation analysis framework. In the algorithm, an effective combination of multiple graphs in each view can be automatically learned in order to better capture the intrinsic geometry structure of each view. Moreover, the algorithm can learn nonlinear correlation features with well discriminating power by maximizing multi-graph intrinsic correlations of different views and simultaneously minimizing intraclass scatter of each view. Extensive experiments on several real-world image datasets have demonstrated the superiority of the algorithm in image recognition.