Incomplete Multi View

Incomplete multi-view learning tackles the challenge of clustering or classifying data where multiple perspectives (views) are available, but some data points are missing across views. Current research focuses on developing robust methods for handling missing data, often employing techniques like imputation (e.g., using autoencoders, K-means, or diffusion models), contrastive learning to leverage consistent and complementary information across views, and graph neural networks to model relationships between data points. These advancements aim to improve the accuracy and reliability of analyses in various applications where incomplete multi-view data is common, such as healthcare monitoring and multimedia analysis.