Abstract Non-negative matrix factorization (NMF) is an effective image representation method and has considerable attention in pattern recognition. The NMF is an unsupervised learning algorithm which can not take into account the label information and the intrinsic geometry structure simultaneously. In this paper,a matrix decomposition method called graph-regularized constrained non-negative matrix factorization (GRCNMF) is proposed,which preserves the label information with resorting to hard constraints,and hence the discriminating ability is improved. Meanwhile,a neighbors graph preserves the intrinsic geometrical structure of the data. The clustering experiments on the COIL20 and ORL image database demonstrate the effectiveness of the GRCNMF compared to other approaches.
SHU Zhen-Qiu,ZHAO Chun-Xia. Graph-Regularized Constrained Non-Negative Matrix Factorization Algorithm and Its Application to Image Representation[J]. , 2013, 26(3): 300-306.
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