联合低秩稀疏的多核子空间聚类算法

针对多核子空间谱聚类算法没有考虑噪声和关系图结构的问题,提出了一种新的联合低秩稀疏的多核子空间聚类算法(JLSMKC)。首先,通过联合低秩与稀疏表示进行子空间学习,使关系图具有低秩和稀疏结构属性;其次,建立鲁棒的多核低秩稀疏约束模型,用于减少噪声对关系图的影响和处理数据的非线性结构;最后,通过多核方法充分利用共识核矩阵来增强关系图质量。7个数据集上的实验结果表明,所提算法JLSMKC在聚类精度(ACC)、标准互信息(NMI)和纯度(Purity)上优于5种流行的多核聚类算法,同时减少了聚类时间,提高了关系图块对角质量。该算法在聚类性能上有较大优势。

Joint low-rank and sparse multiple kernel subspace clustering algorithm

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.