arXiv Machine Learning By Mackenzie R. Neal, Paul D. McNicholas, Arthur White

Turtle shell clustering: A mixture approach to discriminative clustering with applications to flow cytometry and other data

Read the original on arXiv Machine Learning →

arXiv:2604. 23083v2 Announce Type: replace-cross Abstract: Generative approaches to clustering provide information on geometric properties of clusters, whereas discriminative approaches provide boundaries between clusters.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 19

Variational Consensus Monte Carlo for Bayesian Mixture

arXiv:2606. 19643v1 Announce Type: cross Abstract: Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.

By Julie Fendler, Francesca L. Crowe, Tom Marshall, Sylvia Richardson, Paul D. W. Kirk
arXiv Machine Learning
Sep 17

Bracketing Uncertainty in Clustering Under the Manifold Hypothesis

The paper formalizes a geometric tradeoff between ambient separation and sampling gaps to determine when distinct manifold components can be reliably separated in clustering. It introduces a threshold phenomenon for mutual‑k‑nearest‑neighbor graphs, defining an uncertainty zone where the number of clusters cannot be identified. The authors propose Manifold‑Based Clustering (MBC), which outputs a bracket interval quantifying this uncertainty rather than forcing a single cluster count.

By Savik Kinger, Luciano Dyballa, Steven W. Zucker