Computer Science ›› 2020, Vol. 47 ›› Issue (7): 66-70.doi: 10.11896/jsjkx.190600155

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

Subspace Clustering Method Based on Block Diagonal Representation and Neighbor Constraint

GAO Fang-yuan1, WANG Xiu-mei2   

  1. 1 School of Mathematics and System Science,Beihang University,Beijing 102206,China
    2 School of Electronic Engineering,Xidian University,Xi’an 710071,China
  • Received:2019-06-26 Online:2020-07-15 Published:2020-07-16
  • About author:GAO Fang-yuan,born in 2000,underduate student.His current research interests include clustering analysis and image processing.
    WANG Xiu-mei,born in 1978,professor.Her main research interests include statistical machine learning and image processing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61972305,61871308,61772402),College Students’ Innovative Entrepreneurial Training Plan Program and Natural Science Basic Research Plan in Shaanxi Province of China(2019JM-090)

Abstract: Clustering is an important tool for machine learning and data mining,and subspace clustering is a popular method in high-dimensional data analysis.Spectral clustering based subspace clustering method learns the self-representation coefficient matrix of data in subspace,and then the spectral clustering is carried out on the coefficient matrix.It is found that the subspace-based clustering cannot deal with nonlinear problem and neglect the local geometric structure of the data.To this end,this paper proposes a new subspace clustering method which first projects the data to a high-dimensional linear space by a nonlinear mapping function and applies a Laplacian-based manifold regularization constraint on the subspace clustering model to preserve the local structure of the data at the same time.Three kinds of Laplacian matrix are used to establish the different nonlinear subspace clustering models based on manifold regularization and block diagonal constraints.Experimental results on different data sets show the effectiveness of the proposed methods.

Key words: Subspace clustering, Block diagonal constraint, Nonlinear mapping, Manifold regularization

CLC Number: 

  • TP391
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