跨视图敏感信息差异引导的多视图公平聚类
Multi-View Fair Clustering Guided by Cross-View Sensitive Information Discrepancy
- Dalian University of Technology（大连理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对多视图聚类中不同视图敏感依赖差异被忽视的问题，提出由跨视图敏感信息差异引导的公平聚类框架，通过偏置排序非对称对齐与公平正则化，兼顾聚类质量与群体公平。
AI中文摘要：
多视图聚类（MVC）旨在通过利用多个视图的互补信息来揭示潜在的聚类结构。尽管聚类性能取得了显著进展，但当MVC应用于社会敏感场景时，公平性仍然是一个重要问题。近年来的公平多视图聚类方法将公平性约束引入表示学习或聚类分配中。然而，这些方法通常在不同视图上采用大致统一的公平性机制，没有在跨视图学习过程中明确区分它们敏感依赖程度的不同。实际上，不同视图可能编码了显著不同水平的敏感信息。忽略这种跨视图差异可能导致高敏感依赖视图在跨视图学习过程中影响低敏感依赖视图，从而可能降低聚类性能和公平性。为解决这一问题，我们提出了一种由跨视图敏感信息差异引导的新型多视图公平聚类框架。具体而言，我们估计每个视图的敏感依赖程度，并开发了一种基于偏置排序的非对称对齐机制，该机制鼓励敏感依赖程度较高的视图向敏感依赖程度较低的视图学习，同时进一步利用跨视图差异自适应地调节对齐过程。此外，对共识软分配施加公平性正则化，以进一步促进群体公平性。在基准数据集上的大量实验表明，所提出的方法在聚类质量和群体公平性之间实现了良好的平衡。
英文摘要：
Multi-view clustering (MVC) aims to uncover latent cluster structures by exploiting complementary information from multiple views. Despite substantial progress in clustering performance, fairness remains an important concern when MVC is applied to socially sensitive scenarios. Recent fair multi-view clustering methods have introduced fairness constraints into representation learning or clustering assignments. However, these methods generally treat different views under a largely uniform fairness mechanism, without explicitly distinguishing their varying levels of sensitive dependence during cross-view learning. In practice, different views may encode substantially different levels of sensitive information. Ignoring such cross-view discrepancy can allow highly sensitive-dependent views to influence less sensitive-dependent ones during cross-view learning, potentially degrading both clustering performance and fairness. To address this issue, we propose a novel multi-view fair clustering framework guided by cross-view sensitive information discrepancy. Specifically, we estimate the sensitive dependence of each view and develop a bias-ranked asymmetric alignment mechanism that encourages views with higher sensitive dependence to learn from those with lower sensitive dependence, while cross-view discrepancies are further exploited to adaptively regulate the alignment process. Moreover, fairness regularization is imposed on the consensus soft assignments to further promote group fairness. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves a favorable balance between clustering quality and group fairness.
