Solution for supervised graph embedding: A case study

Recently, Graph Embedding Framework has been proposed for feature extraction. Although many algorithms can be used to extract the discriminant transformation of supervised graph embedding, it is still an open issue which algorithm is more robust. In this paper, we first review the classical algorithms which can extract the discriminant transformation of linear discriminant analysis (LDA), and then generalize these classical algorithms for computing the discriminant transformation of supervised graph embedding. Secondly, we theoretically analyze the robustness of these generalized algorithms. Finally, in order to overcome the disadvantages of these generalized algorithms, we propose an effective method, called total space solution for supervised graph embedding (TSS/SGE), to extract the robust discriminant transformation of Supervised Graph Embedding. Extensive experiments and comprehensive comparison on real-world data are performed to demonstrate the robustness of our proposed TSS/SGE.