Pure kernel graph fusion tensor subspace clustering under non-negative matrix factorization framework

Currently, graph-based multi-kernel subspace clustering methods have achieved rich research results in dealing with nonlinear data structures. However, most of the methods still have the following two limitations: (1) the model optimization goal is finding the optimal consensus kernel rather than the optimal affinity graph, which leads to kernel noise always interfering with the learning of the target affinity graph in the optimization process; (2) in the process of finding the optimal affinity graph, only the correlation between different kernel views is exploited, ignoring the higher-order correlation between multiple kernel views. In light of this, we propose a pure kernel graph fusion tensor (PKGT) subspace clustering under non-negative matrix factorization (NMF) framework. Specifically, we first use NMF to construct local affinity feature graphs with standard block diagonal structure for each base kernel matrix, and then achieve adaptive weight assignment by optimizing the inconsistencies among multiple local affinity feature graphs. Finally, we use tensor to capture the higher-order correlation between multiple local affinity feature graphs to provide more feature information for the learning of optimal target affinity graph, which enhances the model clustering performance. Extensive experiments on nine real datasets demonstrate that PKGT has superior clustering performance. The code is available at https://github.com/ZS0124/PKGT.