发表机构
Fujian Normal University; McGill University（福建师范大学; 麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出ACGRL框架，通过梯度反转判别器降低视图可识别性，冻结不变参考表示以分离视图特定信息，并在四个基准数据集上验证了优于现有方法的聚类性能。
AI 中文摘要
多视图聚类旨在捕获跨视图一致性的同时利用视图特定信息。然而，为捕获跨视图一致性而学习的共享表示可能仍保留视图识别信息，从而可能损害跨视图聚类结构的一致性。为解决此问题，我们提出ACGRL，一种用于多视图聚类的对抗一致性引导表示学习框架。ACGRL采用梯度反转视图判别器来降低视图可识别性并获得不变的参考表示。这些表示随后被冻结，为后续学习阶段中从跨视图公共信息中分离视图特定信息提供固定参考。固定的参考表示与学习的视图特定表示拼接，用于重建和聚类，跨视图聚类对齐鼓励一致的聚类分配。在四个基准数据集上的实验表明，与代表性多视图聚类方法相比，ACGRL具有优越的聚类性能。
英文摘要
Multi-view clustering aims to capture cross-view consistency while exploiting view-specific information. However, shared representations learned to capture cross-view consistency may still retain view-identifying information, potentially compromising the consistency of cross-view clustering structures. To address this issue, we propose ACGRL, an adversarial consistency-guided representation learning framework for multi-view clustering. ACGRL employs a gradient-reversal view discriminator to reduce view identifiability and obtain invariant reference representations. These representations are then frozen to provide fixed references for disentangling view-specific information from cross-view common information in the subsequent learning stage. The fixed reference representations are concatenated with the learned view-specific representations for reconstruction and clustering, with cross-view cluster alignment encouraging consistent clustering assignments. Experiments on four benchmark datasets demonstrate the superior clustering performance of ACGRL compared with representative multi-view clustering methods.
Comments5 pages, 4 figures
