A Face Recognition Method Based On Discriminative Hypergraph And Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) has become a popular data representation method and has been widely used in image processing and pattern recognition problems.However, NMF ignores the local geometry of data. Existing simple graph-based trans-ductive learning only considering the image information in pairs, and they are sensitive to the radius parameter used in similarity calculation. Hypergraph learning has been investigated to solve the problems. Hypergraph models the high-order relationship of samples by using the hyperedges to link multiple samples. However, most of the existing hypergraph learning methods are unsupervised methods. Based on the discriminative hypergraph and nonnegative matrix factorization, we propose a new model and solve the new model by using the alternating direction method of multipliers. The new method,together with the nearest neighbor method, is applied to face recognition. Experimental results on several standard face datasets show the effectiveness of our method.