Kernel Block Diagonal Representation Subspace Clustering and Its Convergence Analysis

Authors

Maoshan Liu, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;
Zhicheng Ji, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;
Wang Yan, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;
Jianfeng Wang, Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: Focus on the problems that the linear block diagonal representation subspace clustering cannot effectively handle non-linear visual data, and the regular regularizers cannot directly pursue the k-block diagonal matrix, a kernel block diagonal representation subspace clustering is proposed. In the proposed algorithm, the original input space is mapped into the kernel Hilbert space which is linearly separable, and the spectral clustering is performed in the feature space. The convergence analysis is given, and the strong convex of variables and the boundedness of function is utilized to verify the monotonically decreasing of objective function and the boundedness and convergence of the affinity matrix, which breaks through the difficulty of convergence proof. Compared with other algorithms such as the kernel sparse subspace clustering and the block diagonal representation algorithm tested, the algorithm has achieved the lower clustering error and higher normalized mutual information on Extended Yale B, ORL and MVtec ITODD.

Recommended Citation

Liu, Maoshan; Ji, Zhicheng; Yan, Wang; and Wang, Jianfeng (2021) "Kernel Block Diagonal Representation Subspace Clustering and Its Convergence Analysis," Journal of System Simulation: Vol. 33: Iss. 11, Article 1.
DOI: 10.16182/j.issn1004731x.joss.21-0950
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss11/1

First Page

2533

Revised Date

2021-10-26

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0950

Last Page

2544

CLC

TP391.4

Recommended Citation

Liu Maoshan, Ji Zhicheng, Wang Yan, Wang Jianfeng. Kernel Block Diagonal Representation Subspace Clustering and Its Convergence Analysis[J]. Journal of System Simulation, 2021, 33(11): 2533-2544.

DOI

10.16182/j.issn1004731x.joss.21-0950