Neighbor Contrast-based Multiple Self-supervised Incomplete Multi-view Clustering

Abstract Incomplete multi-view clustering (IMVC) is an important research topic in data mining due to the prevalence of incomplete multi-view data, where samples may be missing in particular views. Although many methods for IMVC have been proposed and achieved encouraging results, these methods ignore the neighbor information for samples with missing views and the global structure of multi-view data, and do not effectively utilize the pseudo labels generated during label allocation. To address these issues, we propose a multiple self-supervised IMVC method based on neighbor contrastive learning (NCMS). Firstly, the missing samples are completed through the transfer of neighbor relationships between views. Then, a two-level node representation learning strategy including view-specific and global-specific contrastive learning is designed to learn the sample features. Next, a multiple self-supervised strategy including intra-view and inter-view self-supervised learning is developed to learn the global representation suitable for clustering tasks. The experimental results on four incomplete multi-view datasets verify the effectiveness of the proposed method, especially on the 100Leaves dataset, when the missing rate is 50%, the ACC and NMI of the proposed method are improved by 6.16% and 4.44% respectively compared with the suboptimal results. Beyond the specific task of IMVC, the proposed method presents a framework for handling incomplete multi-view data, and its core idea could inspire new solutions in other tasks, such as incomplete multi-view classification. The code for this paper is publicly available at Gitee: https://gitee.com/dugking/NCMS .