具有过滤结构融合的对比持续多视图聚类
Contrastive Continual Multi-view Clustering with Filtered Structural Fusion
AI总结：
针对多视图数据按序到达导致的灾难性遗忘问题，提出CCMVC-FSF，利用固定缓冲区保存过滤结构信息并通过对比学习指导新视图划分，理论关联半监督学习与知识蒸馏，实验验证其优越性能。
摘要：
多视图聚类通过提取视图之间的一致和互补信息，在预先收集视图的应用中蓬勃发展。然而，它忽略了数据视图按顺序收集的场景，即实时数据。由于隐私问题或内存负担，在这些情况下，先前的视图随时间推移不可用。一些方法被提出来处理这一问题，但却陷入了稳定性-可塑性困境。具体而言，当获得新视图时，这些方法会遭受先前知识的灾难性遗忘。这种灾难性遗忘问题（CFP）会使一致和互补信息难以获取，并影响聚类性能。为了解决这一问题，我们提出了一种称为具有过滤结构融合的对比持续多视图聚类（CCMVC-FSF）的新方法。具体而言，考虑到数据相关性在聚类中起着至关重要的作用，且先前知识应当指导新视图的聚类过程，我们开发了一个固定大小的数据缓冲区来存储过滤后的结构信息，并利用它通过对比学习指导生成鲁棒的划分矩阵。此外，我们在理论上将CCMVC-FSF与半监督学习和知识蒸馏联系起来。大量实验展示了所提方法的优越性。
英文摘要：
Multi-view clustering thrives in applications where views are collected in advance by extracting consistent and complementary information among views. However, it overlooks scenarios where data views are collected sequentially, i.e., real-time data. Due to privacy issues or memory burden, previous views are not available with time in these situations. Some methods are proposed to handle it but are trapped in a stability-plasticity dilemma. In specific, these methods undergo a catastrophic forgetting of prior knowledge when a new view is attained. Such a catastrophic forgetting problem (CFP) would cause the consistent and complementary information hard to get and affect the clustering performance. To tackle this, we propose a novel method termed Contrastive Continual Multi-view Clustering with Filtered Structural Fusion (CCMVC-FSF). Precisely, considering that data correlations play a vital role in clustering and prior knowledge ought to guide the clustering process of a new view, we develop a data buffer with fixed size to store filtered structural information and utilize it to guide the generation of a robust partition matrix via contrastive learning. Furthermore, we theoretically connect CCMVC-FSF with semi-supervised learning and knowledge distillation. Extensive experiments exhibit the excellence of the proposed method.
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。
