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 k-means++, a novel initialisation strategy for the Minkowski Weighted k-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 this https URL.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2506.06114 [cs.LG] |
| (or arXiv:2506.06114v4 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2506.06114 arXiv-issued DOI via DataCite |
From: Renato Cordeiro de Amorim [view email]
[v1]
Fri, 6 Jun 2025 14:24:41 UTC (30 KB)
[v2]
Thu, 12 Jun 2025 13:48:29 UTC (31 KB)
[v3]
Fri, 13 Jun 2025 14:11:37 UTC (27 KB)
[v4]
Wed, 15 Apr 2026 13:09:36 UTC (26 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。