基于纠缠成对监督的不确定性感知概率约束聚类
Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision
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中文总结 AI 辅助
针对现实中成对监督的不确定性问题，提出ECI-PP框架结合ProbPair目标，在概率约束聚类任务中优于现有DCC方法且鲁棒性强。
中文摘要 AI 辅助
成对约束聚类通常依赖于硬必须链接/不能链接标签，而现实中的成对监督可能是实值的，且包含固有歧义、专家判断和随机噪声。现有深度约束聚类（DCC）方法主要针对与专家无关的硬约束，将软标签仅作为数值处理而非语义处理。我们将此设置形式化为不确定性感知概率约束聚类（UPCC），通过异构观测过程定义典型的偶然目标并分析其条件可识别性。我们引入ProbPair，一种用于概率关系的角成对目标，并构建ECI-PP，一种估计器-校正器-积分器框架，该框架通过信念估计、校正和可靠性感知集成来优化不完善的监督。在具有挑战性的概率监督设置中，对不同基准的实验表明，ECI-PP优于最先进的DCC方法，且在共享默认配置下保持鲁棒性。
英文摘要
Pairwise constrained clustering typically relies on hard must-link/cannot-link labels, whereas realistic pairwise supervision may be real-valued and entangle intrinsic ambiguity, expert judgment, and stochastic corruption. Existing deep constrained clustering (DCC) methods mainly target hard, expert-agnostic constraints, treating soft labels mostly numerically rather than semantically. We formalize this setting as uncertainty-aware probabilistic constrained clustering (UPCC), defining a canonical aleatoric target through a heterogeneous observation process and analyzing its conditional identifiability. We introduce ProbPair, an angular pairwise objective for probabilistic relations, and build ECI-PP, an estimator--corrector--integrator framework that refines imperfect supervision via belief estimation, correction, and reliability-aware integration. Across challenging probabilistic supervision settings, experiments on diverse benchmarks show that ECI-PP outperforms state-of-the-art DCC methods and remains robust with a shared default configuration.
发表机构
- The University of Manchester（曼彻斯特大学）
机构由 AI 辅助整理，请以论文原文为准。
