Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification

As a cross-topic of multi-view learning and multi-label classification, multi-view multi-label classification has gradually gained traction in recent years. The application of multi-view contrastive learning has further facilitated this process; however, the existing multi-view contrastive learning methods crudely separate the so-called negative pair, which largely results in the separation of samples belonging to the same category or similar ones. Besides, plenty of multi-view multi-label learning methods ignore the possible absence of views and labels. To address these issues, in this paper, we propose an incomplete multi-view missing multi-label classification network named RANK. In this network, a…

• NN
  National Natural Science Foundation of China

  Award: 62006059
• SA
  Science and Technology Development Fund

  Award: 0028/2023/RIA1
• BA
  Basic and Applied Basic Research Foundation of Guangdong Province

  Award: 2024A1515030213