TRUST-FS: Tensorized Reliable Unsupervised Multi-View Feature Selection for Incomplete Data

• URL: http://arxiv.org/abs/2509.13192v1
• Date: Tue, 16 Sep 2025 15:54:15 GMT
• Title: TRUST-FS: Tensorized Reliable Unsupervised Multi-View Feature Selection for Incomplete Data
• Authors: Minghui Lu, Yanyong Huang, Minbo Ma, Dongjie Wang, Xiuwen Yi, Tianrui Li,
• Abstract summary: Multi-view unsupervised feature selection (MUFS) has attracted increasing research interest in recent years.<n>Existing methods for incomplete multi-view data are limited to handling missing views and are unable to address the more general scenario of missing variables.<n>We propose a novel MUFS method for incomplete multi-view data termed Reliable Unview Feature Selection ( TRUSTFS)<n>TrustFS simultaneously performs feature selection, missing imputation and view weight learning within a unified factorization framework.
• Score: 15.2618846897032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view unsupervised feature selection (MUFS), which selects informative features from multi-view unlabeled data, has attracted increasing research interest in recent years. Although great efforts have been devoted to MUFS, several challenges remain: 1) existing methods for incomplete multi-view data are limited to handling missing views and are unable to address the more general scenario of missing variables, where some features have missing values in certain views; 2) most methods address incomplete data by first imputing missing values and then performing feature selection, treating these two processes independently and overlooking their interactions; 3) missing data can result in an inaccurate similarity graph, which reduces the performance of feature selection. To solve this dilemma, we propose a novel MUFS method for incomplete multi-view data with missing variables, termed Tensorized Reliable UnSupervised mulTi-view Feature Selection (TRUST-FS). TRUST-FS introduces a new adaptive-weighted CP decomposition that simultaneously performs feature selection, missing-variable imputation, and view weight learning within a unified tensor factorization framework. By utilizing Subjective Logic to acquire trustworthy cross-view similarity information, TRUST-FS facilitates learning a reliable similarity graph, which subsequently guides feature selection and imputation. Comprehensive experimental results demonstrate the effectiveness and superiority of our method over state-of-the-art methods.

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