Journal of Frontiers of Computer Science and Technology ›› 2020, Vol. 14 ›› Issue (11): 1899-1907.DOI: 10.3778/j.issn.1673-9418.2002041

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Discriminative and Graph Regularized Nonnegative Matrix Factorization with Kernel Method

LI Xiangli, ZHANG Ying   

  1. 1. School of Mathematics and Computational Science, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
    2. Guangxi Key Laboratory of Cryptography and Information Security, Guilin, Guangxi 541004, China
    3. Guangxi Key Laboratory of Automatic Testing Technology and Instruments, Guilin, Guangxi 541004, China
    4. Guangxi University Key Laboratory of Data Analysis and Calculation, Guilin, Guangxi 541004, China
  • Online:2020-11-01 Published:2020-11-09



  1. 1. 桂林电子科技大学 数学与计算科学学院,广西 桂林 541004
    2. 广西密码学与信息安全重点实验室,广西 桂林 541004
    3. 广西自动检测技术与仪器重点实验室,广西 桂林 541004
    4. 广西高校数据分析与计算重点实验室,广西 桂林 541004


Nonnegative matrix factorization (NMF) is a popular technique for dimension reduction,which has been extensively applied in image clustering and other fields.However,NMF is an unsupervised approach,which does not take the label information of the data and capture the inherent geometrical structure of data space.And NMF is a linear method that can't be used when the data are nonlinear.To this end,discriminative and graph regularized non-negative matrix factorization with kernel method is proposed,which uses the available label information,incorporates the graph into the NMF to capture the inherent geometrical structure and uses the kernel method to avoid the nonlinear data, and the result of factorization can effectively improve the clustering effect.Iterative initialization of variants of the NMF is random.A “warm start”strategy is adopted to avoid randomness in the result.Clustering experi-ments on several image datasets verify the effectiveness of the algorithm proposed in this paper compared with the other state-of-the-art methods.

Key words: nonnegative matrix factorization (NMF), semi-supervised clustering, graph regular, kernel method



关键词: 非负矩阵分解(NMF), 半监督聚类, 图正则, 核方法