基于高阶随机游走的解耦对比多视图聚类
Decoupled Contrastive Multi-View Clustering with High-Order Random Walks
AI总结：
针对多视图聚类中的假负例与假正例问题，提出DIVIDE，通过高阶随机游走全局识别数据对，并在不同嵌入空间进行视图间和视图内对比学习。
摘要：
近年来，研究者提出了一些鲁棒的对比多视图聚类（MvC）方法，它们从邻域中构造数据对，以缓解假负例问题，即部分簇内样本被错误地当作负样本对。尽管这些方法取得了可观性能，但假负例问题仍远未解决，同时还出现了假正例问题，因为所有邻域内和邻域外样本被简单地分别视为正例和负例。为解决这些问题，我们提出一种新颖的鲁棒方法，称为基于高阶随机游走的解耦对比多视图聚类（DIVIDE）。简而言之，DIVIDE利用随机游走以全局而非局部的方式逐步识别数据对。因此，DIVIDE能够识别邻域内负例和邻域外正例。此外，DIVIDE采用一种新颖的MvC架构，在不同嵌入空间中执行视图间和视图内对比学习，从而提升聚类性能并具备对缺失视图的鲁棒性。为验证DIVIDE的有效性，我们在四个基准数据集上开展了大量实验，在完整和不完整MvC设置下与九种最先进的MvC方法进行比较。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
