Uncertainty-Guided View-Strength-Aware Feature Utilization for Multi-View Classification
Li Lv, Qian Guo, Li Zhang, Liang Du, Bingbing Jiang, Lu Chen, Xinyan Liang
Abstract
In multi-view classification tasks (MVC), each view provides an unique perspective on the data, offering complementary information that can improve classification performance when properly integrated. However, traditional methods typically adopt a uniform processing strategy for all views before fusion, overlooking the fact that different views may require different treatments due to variations in their quality and informativeness. To address this limitation, we propose a novel framework called Uncertainty-Guided View-Strength-Aware Feature Utilization (UVF) for multi-view classification. Our approach introduces a view uncertainty estimation module to quantify the discriminative strength of each view. Based on this estimation, a Differentiated Feature Selector (DFS) adaptively selects features, retaining informative dimensions in weak views while preserving original features in strong views. Furthermore, we employ an uncertainty-guided fusion strategy that assigns dynamic weights to each view
BibTeX
@inproceedings{aaai2026_uncertaintyguide,
title = {Uncertainty-Guided View-Strength-Aware Feature Utilization for Multi-View Classification},
author = {Li Lv and Qian Guo and Li Zhang and Liang Du and Bingbing Jiang and Lu Chen and Xinyan Liang},
booktitle = {AAAI 2026},
year = {2026}
}