The field of multiview learning is moving towards addressing the challenges of incomplete and noisy data. Researchers are exploring new frameworks and methods to improve the performance and reliability of multiview integration and classification. One key direction is the development of innovative approaches to quantify and manage uncertainty in multiview data, including the use of divergence measures and probabilistic models. Another important area of research is the development of efficient and effective methods for clustering and classification in multiview settings, including the use of hierarchical interpolators and correlation-guided techniques. Noteworthy papers in this area include:

• Incomplete Multiview Learning via Wyner Common Information, which proposes an efficient solver for incomplete multiview clustering problems.
• Uncertainty Quantification for Incomplete Multi-View Data Using Divergence Measures, which introduces a new method for estimating uncertainty in multiview classification and clustering tasks.