Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (7): 89-97.doi: 10.11896/jsjkx.220500050

• Database & Big Data & Data Science • Previous Articles     Next Articles

Block Sparse Symmetric Nonnegative Matrix Factorization Based on Constrained Graph Regularization

LIU Wei, DENG Xiuqin, LIU Dongdong, LIU Yulan   

  1. School of Mathematics and Statistics,Guangdong University of Technology,Guangzhou 510000,China
  • Received:2022-05-06 Revised:2022-09-12 Online:2023-07-15 Published:2023-07-05
  • About author:LIU Wei,born in 1996,postgraduate.His main research interests include machine learning and data mining.DENG Xiuqin,born in 1966,professor,master supervisor.Her main research interests include machine learning and data mining.
  • Supported by:
    National Natural Science Foundation of China(12101136),Postgraduate Educational Innovation program of Guangdong(2021SFKC030),Science and Technology Foundation of Guangzhou(202102020273),Regional Join foundation of Guangdong(2020A1515110967) and Open project of Provincial Key Laboratory of Mathematics,Chongqing Normal University(CSSXKFKTQ202002).

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Abstract: The existing algorithms based on symmetric nonnegative matrix factorization(SymNMF) are mostly rely on initial data to construct affinity matrices,and neglect the limited pairwise constraints,so these methods are unable to effectively distinguish similar samples of different categories or learn the geometric features of samples.To solve the above problems,this paper proposes a block sparse symmetric nonnegative matrix factorization based on constrained graph regularization(CGBS-SymNMF).Firstly,the constrained graph matrix is constructed by prior information,which is used to guide the clustering indicator matrix to distinguish different clusters of samples with high similarity.Secondly,pairwise constraint propagation by semidefinite programming(PCP-SDP) is introduced to learn a new sample graph mapping matrix by using pairwise constraints.Finally,a dissimilarity matrix is constructed by cannot-link constraints,which is used to guide a block sparse regular term for enhancing the anti-noise capability of the model.Experimental results demonstrate a higher clustering accuracy and stability of the proposed algorithm.

Key words: Symmetric nonnegative matrix factorization, Affinity matric, Pairwise constraint, Graph regularization, Block sparse

CLC Number: 

  • TP301.6
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