Journal of Guangxi Normal University(Natural Science Edition) ›› 2024, Vol. 42 ›› Issue (4): 137-152.doi: 10.16088/j.issn.1001-6600.2023110202

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Adaptive Semi-supervised Multi-view Clustering Based on Hypergraph Regular NMF

LI Xiangli1,2,3*, MEI Jianping1,3, MO Yuanjian1   

  1. 1. School of Mathematics & Computing Science, Guilin University of Electronic Techology, Guilin Guangxi 541004, China;
    2. Guangxi University Key Laboratory of Data Analysis and Calculation (Guilin University of Electronic Techology), Guilin Guangxi 541004, China;
    3. Center for Applied Mathematics of Guangxi (Guilin University of Electronic Techology), Guilin Guangxi 541004, China
  • Received:2023-11-02 Revised:2024-01-19 Online:2024-07-25 Published:2024-09-05

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Abstract: Although graph regularized non-negative matrix factorization (GNMF) has become the basic framework for a large number of multi-view clustering methods, it is undoubtedly a great challenge to fuse complex data relationships from different views with a simple graph and obtain a consistent discriminative representation at the same time. In order to better deal with the clustering task of multi-view data,a semi-supervised multi-view clustering method based on hypergraph regularized non-negative matrix factorization is proposed. Specifically,by constructing a hypergraph,this method learns the high-order relationships of data from multiple views. In order to make reasonable use of the label information available in the real world,the label constraint is introduced for semi-supervised learning. In addition,this method considers the learning of consistency information and complementarity information at the same time,adopts adaptive measures to distinguish the contributions of different views,and uses an alternating iterative algorithm to optimize the objective function. The comparative experimental results on 7 real datasets show that the proposed algorithm is superior to other classical algorithms and current advanced algorithms in ACC and NMI indicators on 6 datasets.

Key words: hypergraph, non-negative matrix factorization, multi-view clustering, semi-supervised learning

CLC Number:  TP391.1
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