RTMC: A Rubost Trusted Multi-View Classification Framework

Multi-view learning aims to fully exploit the information from multiple sources to obtain better performance than using a single view. However, real-world data often contains a lot of noise, which can have a large impact on multi-view learning. Therefore, it is necessary to identify noise contained in multi-view data to achieve robust and trusted classification. In this paper, we propose a robust trusted multi-view classification framework, RTMC. Our framework uses multi-view affinity and repellence encoding to learn effective latent encodings of multi-view data. We also propose a trust-aware discriminator to estimate trust scores by identifying noise contained in the data. We adopt prototype queues, which store latent encodings of different classes, to accurately identify the noise. Finally, trusted multi-view classification is proposed to jointly predict the trust scores of classification and achieve robust classification results through a trusted fusion strategy. RTMC is validated on six challenging multi-view datasets and the experimental results demonstrate the robustness and effectiveness of our method.