General Solution for Supervised Graph Embedding

摘要

Recently, Graph Embedding Framework has been proposed for feature extraction. However, it is an open issue that how to compute the robust discriminant transformation. In this paper, we first show that supervised graph embedding algorithms share a general criterion (Generalized Rayleigh Quotient). Through novel perspective to Generalized Rayleigh Quotient, we propose a general solution, called General Solution for Supervised Graph Embedding (GSSGE), for extracting the robust discriminant transformation of Supervised Graph Embedding. Finally, extensive experiments on real-world data are performed to demonstrate the effectiveness and robustness of our proposed GSSGE.