Multi View Learning

Multi-view learning aims to improve machine learning model performance by integrating information from multiple data sources or "views" of the same phenomenon. Current research emphasizes robust methods that handle noisy or incomplete data, often employing deep neural networks, graph-based approaches, and novel fusion strategies (e.g., Product-of-Experts, tensor factorization) to combine view-specific information effectively. This field is significant because it allows for more comprehensive and accurate analyses of complex systems, with applications ranging from medical image analysis and remote sensing to anomaly detection and natural language processing. The development of reliable and interpretable multi-view models is a key focus, particularly for safety-critical applications.