Unsupervised Feature Selection Through Group Discovery

TL;DR

This paper introduces GroupFS, an unsupervised feature selection method that discovers and leverages feature groups to improve learning tasks across diverse datasets without requiring labels or predefined groups.

Contribution

GroupFS is a novel end-to-end differentiable framework that jointly discovers feature groups and selects informative ones without relying on prior group definitions or labels.

Findings

Outperforms state-of-the-art unsupervised feature selection methods.

Effectively identifies meaningful feature groups across various datasets.

Enhances clustering performance and interpretability.

Abstract

Unsupervised feature selection (FS) is essential for high-dimensional learning tasks where labels are not available. It helps reduce noise, improve generalization, and enhance interpretability. However, most existing unsupervised FS methods evaluate features in isolation, even though informative signals often emerge from groups of related features. For example, adjacent pixels, functionally connected brain regions, or correlated financial indicators tend to act together, making independent evaluation suboptimal. Although some methods attempt to capture group structure, they typically rely on predefined partitions or label supervision, limiting their applicability. We propose GroupFS, an end-to-end, fully differentiable framework that jointly discovers latent feature groups and selects the most informative groups among them, without relying on fixed a priori groups or label supervision. GroupFS enforces Laplacian smoothness on both feature and sample graphs and applies a group sparsity regularizer to learn a compact, structured representation. Across nine benchmarks spanning images, tabular data, and biological datasets, GroupFS consistently outperforms state-of-the-art unsupervised FS in clustering and selects groups of features that align with meaningful patterns.