Incomplete Multi View Clustering

Incomplete multi-view clustering (IMVC) tackles the challenge of clustering data where multiple views (e.g., different data modalities) are incomplete, meaning some data points are missing in certain views. Research focuses on developing robust methods to leverage complementary information across views while mitigating the impact of missing data, often employing deep learning architectures like autoencoders and graph neural networks, along with techniques such as contrastive learning, data imputation (e.g., KNN, diffusion models, prototype-based methods), and cross-view relation transfer. Effective IMVC algorithms are crucial for various applications dealing with incomplete or noisy multi-source data, improving the accuracy and reliability of clustering results in domains like image analysis, healthcare, and recommendation systems.