Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (11): 172-178.DOI: 10.3778/j.issn.1002-8331.1904-0087

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Multiple Locality-Constrained Self-Representation for Spectral Clustering

JIANG Yirui, PEI Yang, CHEN Lei, WANG Wenle, DAI Jiangyan, YI Yugen   

  1. 1.School of Software, Jiangxi Normal University, Nanchang 330022, China
    2.School of Computer Engineering, Weifang University, Weifang, Shandong 261061, China
  • Online:2020-06-01 Published:2020-06-01



  1. 1.江西师范大学 软件学院,南昌 330022
    2.潍坊学院 计算机工程学院,山东 潍坊 261061


Spectral clustering is an effective subspace clustering method which is widely used in image clustering, image segmentation and others. However, the performance of spectral clustering depends on the construction of graph to a certain extent. Therefore, how to construct an effective graph becomes the key problem in spectral clustering. For the shortcomings of existing graph construction methods, this paper proposes a new method named Multiple Locality Constrained Self Representation (MLCSR) for graph construction. This proposed method considers not only the self-representation ability of the samples, but also the local structure information of the samples. Particularly, it exploits the adaptive weighting to fuse different distance measures when building local constraints. In addition, an iterative optimization algorithm is designed to solve the objective function. Finally, the effectiveness of the proposed method is verified on three standard face databases.

Key words: spectral clustering, locality constraint, multi-criteria fusion, graph construction



关键词: 谱聚类, 局部约束, 多准则融合, 图构建