SOURCE-LINKED INTELLIGENCE

A Manifold-Aware Topic Modeling Approach via Rank-Based Prototypes

arXiv · AI, language, vision and robotics · article · Aug 29, 2026 · UTC

Recent topic models leverage pretrained embeddings, but neural architectures produce latent representations without grounding in specific texts, and clustering-based pipelines assign representative documents only post hoc, relying on absolute distances distorted by hubness and anisotropy in high-dimensional spaces. We introduce MARETopic, a training-free framework that casts topic discovery as rank-based prototype selection. After projecting embeddings onto a low-dimensional manifold, MARETopic builds ranked lists encoding ordinal neighborhood structure. A greedy algorithm selects exactly K ex

Read original source ↗ Open in workspace

recordType

paper

region

Global