In recent, some robust contrastive multi-view clustering (MvC) methods have
been proposed, which construct data pairs from neighborhoods to alleviate the
false negative issue, i.e., some intra-cluster samples are wrongly treated as
negative pairs. Although promising performance has been achieved by these
methods, the false negative issue is still far from addressed and the false
positive issue emerges because all in- and out-of-neighborhood samples are
simply treated as positive and negative, respectively. To address the issues,
we propose a novel robust method, dubbed decoupled contrastive multi-view
clustering with high-order random walks (DIVIDE). In brief, DIVIDE leverages
random walks to progressively identify data pairs in a global instead of local
manner. As a result, DIVIDE could identify in-neighborhood negatives and
out-of-neighborhood positives. Moreover, DIVIDE embraces a novel MvC
architecture to perform inter- and intra-view contrastive learning in different
embedding spaces, thus boosting clustering performance and embracing the
robustness against missing views. To verify the efficacy of DIVIDE, we carry
out extensive experiments on four benchmark datasets comparing with nine
state-of-the-art MvC methods in both complete and incomplete MvC settings.

通过随机游走在全局范围内逐步识别数据对，DIVIDE 使用新颖的多视角对比学习架构，在不同的嵌入空间中执行视图内和视图间对比学习，从而提升聚类性能，并增加对缺失视图的鲁棒性。