Cross-view Joint Learning for Mixed-Missing Multi-view Unsupervised Feature Selection

• URL: http://arxiv.org/abs/2511.12261v1
• Date: Sat, 15 Nov 2025 15:34:52 GMT
• Title: Cross-view Joint Learning for Mixed-Missing Multi-view Unsupervised Feature Selection
• Authors: Zongxin Shen, Yanyong Huang, Dongjie Wang, Jinyuan Chang, Fengmao Lv, Tianrui Li, Xiaoyi Jiang,
• Abstract summary: We propose CLIM-FS, a novel IMUFS method designed to address the mixed-missing problem.<n>We integrate the imputation of both missing views and variables into a feature selection model based on nonnegative matrix factorization.<n>We fully leverage consensus cluster structure and cross-view local geometrical structure to enhance the synergistic learning process.
• Score: 24.037106656954666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Incomplete multi-view unsupervised feature selection (IMUFS), which aims to identify representative features from unlabeled multi-view data containing missing values, has received growing attention in recent years. Despite their promising performance, existing methods face three key challenges: 1) by focusing solely on the view-missing problem, they are not well-suited to the more prevalent mixed-missing scenario in practice, where some samples lack entire views or only partial features within views; 2) insufficient utilization of consistency and diversity across views limits the effectiveness of feature selection; and 3) the lack of theoretical analysis makes it unclear how feature selection and data imputation interact during the joint learning process. Being aware of these, we propose CLIM-FS, a novel IMUFS method designed to address the mixed-missing problem. Specifically, we integrate the imputation of both missing views and variables into a feature selection model based on nonnegative orthogonal matrix factorization, enabling the joint learning of feature selection and adaptive data imputation. Furthermore, we fully leverage consensus cluster structure and cross-view local geometrical structure to enhance the synergistic learning process. We also provide a theoretical analysis to clarify the underlying collaborative mechanism of CLIM-FS. Experimental results on eight real-world multi-view datasets demonstrate that CLIM-FS outperforms state-of-the-art methods.

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