Trusted Multi-View Classification

TL;DR

This paper introduces a trusted multi-view classification method that dynamically assesses view quality and integrates evidence at the distribution level to improve reliability and robustness, especially for out-of-distribution samples.

Contribution

It presents a novel framework that combines evidence from multiple views using Dirichlet distribution and Dempster-Shafer theory for reliable uncertainty estimation in multi-view classification.

Findings

Improves classification accuracy, reliability, and robustness.

Effectively detects out-of-distribution samples.

Outperforms existing methods in experimental evaluations.

Abstract

Multi-view classification (MVC) generally focuses on improving classification accuracy by using information from different views, typically integrating them into a unified comprehensive representation for downstream tasks. However, it is also crucial to dynamically assess the quality of a view for different samples in order to provide reliable uncertainty estimations, which indicate whether predictions can be trusted. To this end, we propose a novel multi-view classification method, termed trusted multi-view classification, which provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The algorithm jointly utilizes multiple views to promote both classification reliability and robustness by integrating evidence from each view. To achieve this, the Dirichlet distribution is used to model the distribution of the class probabilities, parameterized with evidence from different views and integrated with the Dempster-Shafer theory. The unified learning framework induces accurate uncertainty and accordingly endows the model with both reliability and robustness for out-of-distribution samples. Extensive experimental results validate the effectiveness of the proposed model in accuracy, reliability and robustness.