Causally-Aware Unsupervised Feature Selection Learning

• URL: http://arxiv.org/abs/2410.12224v1
• Date: Wed, 16 Oct 2024 04:41:38 GMT
• Title: Causally-Aware Unsupervised Feature Selection Learning
• Authors: Zongxin Shen, Yanyong Huang, Minbo Ma, Tianrui Li,
• Abstract summary: Unsupervised feature selection (UFS) has recently gained attention for its effectiveness in processing unlabeled high-dimensional data. Previous graph-based methods fail to account for the differing impacts of non-causal and causal features in constructing the similarity graph. A novel UFS method, called Causally-Aware UnSupErvised Feature Selection learning (CAUSE-FS), is proposed.
• Score: 3.8734875101038706
• License:
• Abstract: 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.

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