Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision

Abstract: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 relation 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.

Accepted at NeurIPS 2026 (Spotlight). Camera-ready revision; 44 pages, including appendices

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2608.12027 [cs.LG]

(or arXiv:2608.12027v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.12027 Focus to learn more arXiv-issued DOI via DataCite

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