A Direct LGE Algorithm and Its Application to Face Recognition
直接线性图嵌入算法及其在人脸识别中的应用

The algorithms of Graph Embedding model the manifold of data set by an undirected weighted graph. Some manifold learning algorithms can be unified by this framework according to the respective weighted matrix. For the small sample size problem, Linearization of Graph Embedding (LGE) needs to project the data to the PCA subspace. In this paper, a Direct LGE (DLGE) algorithm is proposed which can directly extract features from the data set. Moreover, DLGE employs the least-squares orthogonalization for the preserving feature vectors. The simulation results on several face databases show that DLGE has better ability for face representation, and also demonstrate the effectiveness and robustness of our proposed algorithm.