Incomplete multi-view clustering has gained considerable attention in recent years due to the prevalence of incomplete multi-view data in real-world applications. However, existing methods often struggle to effectively deal with large-scale datasets, particularly those with a significant number of missing instances. To address these issues, we propose a novel method called Hubness-Enabled Clustering and Recovery for Large-Scale Incomplete Multi-View Data (HENRI). HENRI utilizes the consensus hubs of all views to identify informative anchors to handle large-scale incomplete datasets. Furthermore, it incorporates a novel sample-level fusion strategy that effectively integrates information from all views, leading to remarkable outcomes in both cluster formation and missing data reconstruction. HENRI demonstrates exceptional capability in capturing the underlying structures of the data and recovering missing information, even when faced with a significant number of instances with incomplete data in partial views. To validate its effectiveness, we conducted experiments on 6 complete datasets and 31 incomplete datasets, comparing against 11 baseline methods. The results are impressive, demonstrating the superior performance of HENRI over the state-of-the-art methods.

Multiview spectral clustering, which stands out with its remarkable clustering performance, has drawn increasing research attention. Its core is properly weighing the contribution of different views and comprehensively utilizing multiview information in the clustering process. Although existing methods, like exponential decay and root loss, have achieved significant progress, they still have limitations in their weighting scheme, application generalization, and model efficiency. To handle these limitations, we propose a novel Similarity-Induced Weighted Consensus Laplacian matrix learning method for multiview clustering (MC), named SIWCL. This method has two distinctive features: 1) instead of conventional Laplacian matrix learning, SIWCL resorts to consensus Laplacian matrix learning as the MC framework for effectively exploiting complementary information from multiple views and 2) we argue that there could be outlier views that exhibit an uneven similarity distribution with other views, and equally treating them with other views can hurt model performance. Therefore, beyond consensus Laplacian matrix learning, SIWCL introduces a novel weighting strategy that adaptively assigns weights to views according to their consistency with other views based on the multiview similarity matrices. Notably, this novel method has only one hyperparameter and closed-form solutions, greatly improving the efficiency and generalization. Experiments show that the weights obtained by the proposed weighting strategy are correlated to the quality of the clustering structure. The comparisons between the proposed method and other state-of-the-art baseline methods over eight datasets demonstrate the robustness and superior clustering performance of SIWCL.

Multiview spectral clustering aims to separate data into different clusters efficiently by the use of multiview information. Many studies learn the affinity matrix from the original high-dimensional data, whose noise goes against the clustering results. Besides, some methods based on self-representation subspace clustering have a high time complexity. In this paper, we propose a simple, yet effective, and efficient method named Kernel-based Low-rank Tensorized Multiview Spectral Clustering (KLTMSC) to address these issues. Instead of using the original data to get the affinity matrix, KLTMSC learns the affinity matrix from kernel representation of the high-dimensional data to reduce the noisy information. Furthermore, to be robust to noise, the low-rank tensor is learned in the process of exploring the high-order correlations between data. Experiments on real-world data sets show that our method not only yields better results but also is quite time-saving compared with other state-of-the-art models.