Multi-view learning aims to combine multiple features to achieve more
comprehensive descriptions of data. Most previous works assume that multiple
views are strictly aligned. However, real-world multi-view data may contain
low-quality conflictive instances, which show conflictive information in
different views. Previous methods for this problem mainly focus on eliminating
the conflictive data instances by removing them or replacing conflictive views.
Nevertheless, real-world applications usually require making decisions for
conflictive instances rather than only eliminating them. To solve this, we
point out a new Reliable Conflictive Multi-view Learning (RCML) problem, which
requires the model to provide decision results and attached reliabilities for
conflictive multi-view data. We develop an Evidential Conflictive Multi-view
Learning (ECML) method for this problem. ECML first learns view-specific
evidence, which could be termed as the amount of support to each category
collected from data. Then, we can construct view-specific opinions consisting
of decision results and reliability. In the multi-view fusion stage, we propose
a conflictive opinion aggregation strategy and theoretically prove this
strategy can exactly model the relation of multi-view common and view-specific
reliabilities. Experiments performed on 6 datasets verify the effectiveness of
ECML.

提出了一种可靠性冲突多视图学习 (RCML) 问题，并提出了 Evidential Conflictive Multi-view Learning (ECML) 方法，该方法首先学习视图特定证据，然后构建视图特定观点，最后在多视图融合阶段提出了冲突观点聚合策略。实验证实了 ECML 的有效性。