  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.
