Robust automated graph regularized discriminative non-negative matrix factorization

Non-negative matrix factorization (NMF) and its variants have been widely employed in clustering and classification task. However, the existing methods do not consider robustness, adaptive graph learning and discrimination information at the same time. To solve this problem, a new nonnegative matrix factorization method is proposed, which is called robust automated graph regularized discriminative non-negative matrix factorization (RAGDNMF). Specifically, L-2,L-1 norm is used to describe the reconstruction error, the appropriate Laplacian graph is automatically learned and the label information of the training set is added as the regularization term. The ultimate goal is to learn a good projection matrix, which can remove redundant information while preserving the effective components. In addition, we give the multiplicative updating rules for solving optimization problems and convergence proof of objective function. Face recognition experiments on four benchmark datasets show the effectiveness of our proposed method.

Automated summary

The new method RAGDNMF combines robustness, adaptive graph learning and discrimination information, aiming to learn a good projection matrix for removing redundant information while preserving effective components. Face recognition experiments on four benchmark datasets demonstrate the effectiveness of the proposed method.