Block Kernel Nonnegative Matrix Factorization and Its Application to Face Recognition

Traditional nonnegative matrix factorization (NMF) is an unsupervised method for linear feature extraction. Recently, NMF with block strategy is shown to be able to extract more sparse and discriminative information of the images. To enhance the discriminative power of NMF, this paper proposes a block kernel nonnegative matrix factorization (BKNMF) based on the kernel theory and block technique. Kernel method is an effective way to model the nonlinear relations, which could help us to extract nonlinear features. Furthermore, we make use of the class label information to reduce the within-class distance for further improving the discriminative performance. We theoretically analyze the convergence of the proposed method. Three face databases, namely Yale, ORL and FERET databases, are chosen for evaluations. Compared with some state-of-the-art methods, experimental results show that our BKNMF approach achieves superior performance.