Adaptive Graph Nonnegative Matrix Factorization with the Self-paced Regularization

Nonnegative matrix factorization (NMF) is a popular approach to extract intrinsic features from the original data. As the nonconvexity of NMF formulation, it always leads to degrade the performance. To alleviate the defect, in this paper, the self-paced regularization is introduced to find a better factorized matrices by sequentially selecteing data in the learning process. Additionally, to find the low-dimensional manifold embeded in the high-dimensional space, adaptive graph is introduced by using dynamic neighbors assignment. An alternating iterative algorithm is designed to sovle the proposed mathematical factorization formulation. The experimental results are given to show the effectiveness of the proposed approach in comparison with state-of-the-art algorithms on six public datasets.