Structure-Aware Prototype Guided Trusted Multi-View Classification

TL;DR

This paper introduces a prototype-guided multi-view classification framework that enhances trustworthiness and efficiency by modeling intra-view relationships with prototypes and aligning inter-view structures, improving consistency and robustness.

Contribution

It proposes a novel prototype-based approach for modeling intra-view neighbor relations and aligning multi-view structures, addressing computational costs and inconsistency issues in existing TMVC methods.

Findings

Achieves competitive classification performance on public datasets.

Improves robustness and consistency in multi-view classification.

Reduces computational complexity compared to existing methods.

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.