Scalable unsupervised feature selection via weight stability
Abstract
Unsupervised feature selection is critical for improving clustering performance in high-dimensional data, where irrelevant features can obscure meaningful structure. In this work, we introduce the Minkowski weighted -means++, a novel initialisation strategy for the Minkowski Weighted -means. Our initialisation selects centroids probabilistically using feature relevance estimates derived from the data itself. Building on this, we propose two new feature selection algorithms, FS-MWK++, which aggregates feature weights across a range of Minkowski exponents to identify stable and informative features, and SFS-MWK++, a scalable variant based on subsampling. We support our approach with a theoretical analysis, demonstrating that, under explicit assumptions on noise features and cluster structure, relevant features are assigned consistently higher weights than noise features across a range of Minkowski exponents. Our software can be found at https://github.com/xzhang4-ops1/FSMWK.
Cite
@article{arxiv.2506.06114,
title = {Scalable unsupervised feature selection via weight stability},
author = {Xudong Zhang and Renato Cordeiro de Amorim},
journal= {arXiv preprint arXiv:2506.06114},
year = {2026}
}