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

2025-09-16

AI Summary

To address the performance degradation of unsupervised multi-view feature selection (MUFS) caused by *feature-level missingness*—rather than merely view-level incompleteness—in incomplete multi-view data, this paper proposes a unified end-to-end framework that *jointly optimizes feature selection, missing value imputation, and view-weight learning*. Innovatively integrating *adaptive weighted CP tensor decomposition* with *subjective logic modeling*, the method enables cross-view missing-pattern-aware collaborative imputation and constructs a trustworthy similarity graph, thereby overcoming the task decoupling limitation inherent in conventional two-stage approaches. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method significantly outperforms existing state-of-the-art methods, substantially enhancing both robustness and discriminability of feature selection under incomplete multi-view settings.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Handles incomplete multi-view data with missing variables
• Integrates feature selection and missing value imputation jointly
• Learns reliable similarity graphs for improved feature selection

Innovation

• Tensor factorization for feature selection
• Simultaneous imputation and selection integration
• Subjective Logic for reliable similarity graph