Structure-Aware Prototype Guided Trusted Multi-View Classification

• URL: http://arxiv.org/abs/2511.21021v1
• Date: Wed, 26 Nov 2025 03:33:42 GMT
• Title: Structure-Aware Prototype Guided Trusted Multi-View Classification
• Authors: Haojian Huang, Jiahao Shi, Zhe Liu, Harold Haodong Chen, Han Fang, Hao Sun, Zhongjiang He,
• Abstract summary: Trustworthy multi-view classification (TMVC) addresses the challenge of achieving reliable decision-making in complex scenarios.<n>Existing TMVC approaches rely on globally dense neighbor relationships to model intra-view dependencies.<n>We propose a novel TMVC framework that introduces prototypes to represent the neighbor structures of each view.
• Score: 30.492395941702384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trustworthy multi-view classification (TMVC) addresses the challenge of achieving reliable decision-making in complex scenarios where multi-source information is heterogeneous, inconsistent, or even conflicting. Existing TMVC approaches predominantly rely on globally dense neighbor relationships to model intra-view dependencies, leading to high computational costs and an inability to directly ensure consistency across inter-view relationships. Furthermore, these methods typically aggregate evidence from different views through manually assigned weights, lacking guarantees that the learned multi-view neighbor structures are consistent within the class space, thus undermining the trustworthiness of classification outcomes. To overcome these limitations, we propose a novel TMVC framework that introduces prototypes to represent the neighbor structures of each view. By simplifying the learning of intra-view neighbor relations and enabling dynamic alignment of intra- and inter-view structure, our approach facilitates more efficient and consistent discovery of cross-view consensus. Extensive experiments on multiple public multi-view datasets demonstrate that our method achieves competitive downstream performance and robustness compared to prevalent TMVC methods.

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