JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (3): 67-76.doi: 10.6040/j.issn.1671-9352.4.2020.218

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Semi-supervised spectral clustering algorithm based on L2,1 norm and manifold regularization terms

YANG Ting1,2, ZHU Heng-dong1, MA Ying-cang1, WANG Yi-rui2, YANG Xiao-fei1*   

  1. 1. School of Science, Xian Polytechnic University, Xian 710600, Shaanxi, China;
    2. School of Mathematics and Statistics, Ankang University, Ankang 725000, Shaanxi, China
  • Published:2021-03-16

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Abstract: The spectral clustering algorithm is affected by the similarity matrix and not using prior information, which makes the clustering results with great limitations. For this problem, we propose a semi-supervised spectral clustering algorithm based on L2,1 norm and manifold regularization terms. With the help of robustness in L2,1 norms, a reasonable similarity matrix is learned. In addition, full use of supervisory information not only is added in the initial similarity matrix, but also is used in manifold regularization term to adjust the model, thereby improving the clustering effect. The clustering results of the proposed clustering algorithm on artificial data sets and real data sets are more effective than other clustering algorithms in most cases.

Key words: L2,1 norm, manifold regularization term, spectral clustering, semi-supervised learning

CLC Number: 

  • TP311
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