Joint low-rank and sparse multiple kernel subspace clustering algorithm
LI Xingfeng1, HUANG Yuqing1, REN Zhenwen2
1. School of Information Engineering, Southwest University of Science and Technology, Mianyang Sichuan 621010, China 2. School of National Defense Science and Technology, Southwest University of Science and Technology, Mianyang Sichuan 621010, China
Abstract：Since the methods of multiple kernel subspace spectral clustering do not consider the problem of noise and relation graph structure, a novel Joint Low-rank and Sparse Multiple Kernel Subspace Clustering algorithm (JLSMKC) was proposed. Firstly, with combination of low-rank and sparse representation for subspace learning, the relation graph obtained the attribute of low-rank and sparse structure. Secondly, a robust multiple kernel low-rank and sparsity constraint model was constructed to reduce the influence of noise on the relation graph and handle the nonlinear structure of data. Finally, the quality of relation graph was enhanced by making full use of the consensus kernel matrix by multiple kernel approach. The experimental results on seven datasets show that the proposed JLSMKC is better than five popular multiple kernel clustering algorithms in ACCuracy (ACC), Normalized Mutual Information (NMI) and Purity. Meanwhile, the clustering time is reduced and the block diagonal quality of relation graph is improved. JLSMKC has great advantages in clustering performance.
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2020, Vol. 40 
