Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 80, Issue 10, Pages 14867-14886Publisher
SPRINGER
DOI: 10.1007/s11042-020-10410-w
Keywords
Non-negative matrix factorization; Robust constraint; Adaptive graph learning; Discriminative information; Face recognition
Categories
Funding
- National Natural Science Foundation of China [61906098, 61701258, 61872190, 61906099, 61972210]
- Natural Science Foundation of the Jiangsu Higher Education Institutions of China [18KJB520034]
Ask authors/readers for more resources
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.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available