本代码实现基于成对约束的半监督图嵌入算法-Following the intuition that the image variation of

faces can be effectively modeled by low dimensional

linear spaces, we propose a novel linear subspace

learning method for face analysis in the fr a mework of

graph embedding model, called Semi-supervised

Graph Embedding (SGE). This algorithm builds an

adjacency graph which can best respect the geometry

structure inferred from the must-link pairwise

constraints, which specify a pair of instances belong to

the same class. The projections are obtained by

preserving such a graph structure. Using the notion of

graph Laplacian, SGE has a closed solution of an

eigen-problem of some specific Laplacian matrix and

therefore it is quite efficient. Experimental results on

Yale standard face database demonstrate the

effectiveness of our proposed algorithm.