Multi-view clustering via view-specific consensus kernelized graph learning

Multi-view clustering has received extensive and in-depth research attention in recent years owing to its ability to reflect the nature of the real world from multiple perspectives. Kernel-based methods and subspace learning- based methods are two important categories of multi-view clustering. Compared with subspace-based algorithms, kernel-based algorithms can better address nonlinear relationships in feature spaces. However, the current kernel-based algorithms focus mainly on the diversity of different kernels, and obtaining the optimal kernel via linear combinations of multiple kernels, ignoring the cross-view information and space information in the original feature spaces. To address this issue, our paper proposes a novel algorithm named MC-VCKGL. Specifically, we first obtain view-specific consensus kernelized graphs of each view through kernel-based self- representation learning and by using the kernel trick. Moreover, Laplacian constraints are applied to maintain smoothness in the raw feature space of each view. We stack these kernelized graphs together to obtain a tensor, and then rotate this tensor and apply tensor nuclear norm constraints. As a result, the cross-view complementary information can be explored. We apply our algorithm to seven open datasets, including both text and image datasets. Experiments show that our method outperforms most state-of-the-art multi-view clustering algorithms.