Scalable unsupervised feature selection via weight stability

Code

• github.com/xzhang4-ops1/fsmwk
  OfficialIn papernone★ 2

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 guarantee under mild assumptions and extensive experiments showing that our methods consistently outperform existing alternatives. Our software can be found at https://github.com/xzhang4-ops1/FSMWK.

Tasks

Reproductions