Supplyment to the article"Relational contrastive multi-view clustering"

Nevertheless, many existing approaches fail to account for such cross-view structural consistency, which may cause semantic confusion under different views. Lack of global feature and semantic guidance: Most existing MVCs primarily depend on local representation alignment or pseudo-label supervision for optimization [6–9]. For instance, a selective alignment strategy is introduced [10], which aligns representations of different views at the sample level to boost clustering accuracy.

While these approaches can enhance clustering performance, they typically lack a guidance mechanism that leverages global feature and semantic information, making it challenging to fully integrate the comprehensive information across multiple views. Contribution. To tackle the above problems, we propose a novel relational contrastive MVC (RCMVC) method, as shown in Fig. 1.

The main contributions of this work are: 1) We propose a novel RCMVC method, achieving dual alignment at both the sample and relation levels. The former guides the representation learning for individual samples, while the latter captures relational consistency among different samples. 2) We introduce a relation-level contrastive module that explicitly aligns pairwise structural relationships across views. 3) Global feature information and semantic information are incorporated to guide local view learning."