mlsanityData to 13 Oct 2026

k-MLE, k-Bregman, k-VARs: Theory, Convergence, Computation

arXiv 2409.06938 ↗First posted 11 Sep 2024stat.ML cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

We develop hard clustering based on likelihood rather than distance and prove convergence. We also provide simulations and real data examples.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 2 of its 45 references that are in the map. First posted on arXiv 11 Sep 2024.

Builds on

2 of 45 references.

References · 2

  1. 2018Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Featurescited by 37
  2. 2013An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clusteringcited by 17

Built on by

Not cited yet.