Causally-Aware Unsupervised Feature Selection Learning

Zongxin Shen

doi:10.1109/TIP.2026.3654354

Causally-Aware Unsupervised Feature Selection Learning

2026-01-23

0

PRE

AI

DOI：10.1109/TIP.2026.3654354

Original

Original request for help

Save

Abstract

En 中文

Unsupervised feature selection (UFS) has recently gained attention for its effectiveness in processing unlabeled high-dimensional data. However, existing methods overlook the intrinsic causal mechanisms within the data, resulting in the selection of irrelevant features and poor interpretability. Additionally, previous graph-based methods fail to account for the differing impacts of non-causal and causal features in constructing the similarity graph, which leads to false links in the generated graph. To address these issues, a novel UFS method, called Causally-Aware UnSupErvised Feature Selection learning (CAUSE-FS), is proposed. CAUSE-FS introduces a novel causal regularizer that reweights samples to balance the confounding distribution of each treatment feature. This regularizer is subsequently integrated into a generalized unsupervised spectral regression model to mitigate spurious associations between features and clustering labels, thus achieving causal feature selection. Furthermore, CAUSE-FS employs causality-guided hierarchical clustering to partition features with varying causal contributions into multiple granularities. By integrating similarity graphs learned adaptively at different granularities, CAUSE-FS increases the importance of causal features when constructing the fused similarity graph to capture the reliable local structure of data. Extensive experimental results demonstrate the superiority of CAUSE-FS over state-of-the-art methods, with its interpretability further validated through feature visualization.

Keywords:

Unsupervised feature selection

multi-granular adaptive graph learning

confounder balancing

causal features

Causally-Aware Unsupervised Feature Selection Learning

Abstract

Journal

Organization