View Specific

Multi-view learning aims to integrate information from multiple data sources (views) to create more robust and informative representations than using any single view alone. Current research focuses on developing methods that effectively disentangle view-specific and view-consistent information, often employing techniques like contrastive learning, matrix factorization, and autoencoders within unified frameworks. These advancements improve the efficiency and accuracy of clustering and classification tasks, impacting diverse fields by enabling better analysis of complex datasets with heterogeneous information. The ultimate goal is to create representations that are both accurate and interpretable, maximizing the utility of multi-source data.