Analysis of solution for supervised graph embedding

Recently, Graph Embedding Framework has been proposed for feature extraction. However, it is still an open issue on how to compute robust discriminant transformation for this purpose. In this paper, we show that supervised graph embedding algorithms share a general criterion. Based on the analysis of this criterion, we propose a general solution, called General Solution for Supervised Graph Embedding (GSSGE), for extracting the robust discriminant transformation of Supervised Graph Embedding. Then, we analyze the superiority of our algorithm over traditional algorithms. Extensive experiments on both artificial and real-world data are performed to demonstrate the effectiveness and robustness of our proposed GSSGE.