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Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision — arXiv:2608.12027

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.12027
License (as archived): CC0-1.0
Archived: 2026-10-19T05:16:22.501Z
SHA-256 of the archived text: 4d2d165fc2b4dcd5adfbcd9d4b4870e06121b8fd396aff811b579470ccbcea83
Stable URL of this page: https://metraipolis.com/documento/dc_3063699bfb60

How to cite

Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision — arXiv:2608.12027 — arXiv (en), https://arxiv.org/abs/2608.12027 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_3063699bfb60 (Metraipolis archive dc_3063699bfb60, sha256 4d2d165fc2b4dcd5adfbcd9d4b4870e06121b8fd396aff811b579470ccbcea83)

BibTeX

@misc{dc3063699bfb60,
  title = {Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision — arXiv:2608.12027},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.12027},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_3063699bfb60; sha256 4d2d165fc2b4dcd5adfbcd9d4b4870e06121b8fd396aff811b579470ccbcea83},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.12027. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-13 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.12027 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:08:46.754Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-13?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).