Beyond Correlation: Causal Multi-View Unsupervised Feature Selection Learning

• URL: http://arxiv.org/abs/2509.13763v1
• Date: Wed, 17 Sep 2025 07:22:22 GMT
• Title: Beyond Correlation: Causal Multi-View Unsupervised Feature Selection Learning
• Authors: Zongxin Shen, Yanyong Huang, Bin Wang, Jinyuan Chang, Shiyu Liu, Tianrui Li,
• Abstract summary: Multi-view unsupervised feature selection (MUFS) has recently received increasing attention for its promising ability in dimensionality reduction on unlabeled data.<n>Existing MUFS methods typically select discriminative features by capturing correlations between features and clustering labels.<n>We introduce a novel structural causal model, which reveals that existing methods may select irrelevant features because they overlook spurious correlations caused by confounders.
• Score: 14.848818417654316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view unsupervised feature selection (MUFS) has recently received increasing attention for its promising ability in dimensionality reduction on multi-view unlabeled data. Existing MUFS methods typically select discriminative features by capturing correlations between features and clustering labels. However, an important yet underexplored question remains: \textit{Are such correlations sufficiently reliable to guide feature selection?} In this paper, we analyze MUFS from a causal perspective by introducing a novel structural causal model, which reveals that existing methods may select irrelevant features because they overlook spurious correlations caused by confounders. Building on this causal perspective, we propose a novel MUFS method called CAusal multi-view Unsupervised feature Selection leArning (CAUSA). Specifically, we first employ a generalized unsupervised spectral regression model that identifies informative features by capturing dependencies between features and consensus clustering labels. We then introduce a causal regularization module that can adaptively separate confounders from multi-view data and simultaneously learn view-shared sample weights to balance confounder distributions, thereby mitigating spurious correlations. Thereafter, integrating both into a unified learning framework enables CAUSA to select causally informative features. Comprehensive experiments demonstrate that CAUSA outperforms several state-of-the-art methods. To our knowledge, this is the first in-depth study of causal multi-view feature selection in the unsupervised setting.

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