Unsupervised Feature Selection Through Group Discovery

2025-11-12

AI Summary

Unsupervised feature selection (FS) faces a fundamental challenge: existing methods typically evaluate features in isolation, failing to capture semantically coherent feature groups—such as image pixel blocks or functionally connected brain regions—that exhibit synergistic discriminative power, and often rely on predefined groupings or label supervision. To address this, we propose GroupFS—the first end-to-end differentiable framework for unsupervised FS that jointly learns latent feature group structures and selects the most discriminative groups, without requiring prior grouping knowledge or labels. Its core innovation lies in integrating Laplacian smoothing constraints on both the feature graph and the sample graph, coupled with group-wise sparsity regularization, enabling gradient-based optimization of structured, compact representations. Evaluated on nine cross-domain benchmark datasets (image, tabular, and biological), GroupFS consistently improves clustering performance and identifies semantically meaningful, interpretable feature groups—advancing unsupervised FS toward structure-aware, end-to-end joint learning.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Representation Learning for Vision
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Identifies latent feature groups without predefined partitions or supervision
• Selects informative feature groups rather than evaluating features individually
• Improves unsupervised feature selection for clustering across diverse data types

Innovation

• Discovers latent feature groups without supervision
• Applies Laplacian smoothness and group sparsity
• Selects informative groups instead of isolated features