让聚类模型学会处理模糊的成对约束，提升实际场景下的鲁棒性。
Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision
- 引入概率关系的角距离目标，建模成对约束的不确定性
- 在多个基准上超越现有方法，且无需调参即保持稳定性能
- 适合有噪声或主观判断的成对标注场景，如医疗数据聚类
成对约束聚类通常依赖硬性的必须链接/不能链接标签，而现实中的成对监督可能为实值，混合内在模糊性、专家判断与随机噪声。现有深度约束聚类（DCC）方法多针对无专家依赖的硬约束，对软标签仅作数值处理而非语义理解。本文将其形式化为不确定性感知的概率约束聚类（UPCC），通过异质观测过程定义典型的随机性目标，并分析其条件可辨识性。提出ProbPair——一种用于概率关系的角距离目标函数；构建ECI-PP框架，包含信念估计、修正与可靠性感知集成三个模块，以优化不完善监督。在多种具有挑战性的概率监督设置下，实验表明ECI-PP优于当前最优DCC方法，且使用统一默认配置仍具鲁棒性。
原文摘要 · Abstract (English)
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.
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