MULTI-VIEW LEARNING

Multi-view learning is a machine learning framework in which data are represented by multiple distinct sets of features, each set referred to as a specific view. The approach aims to enhance the model's generalization ability and predictive accuracy by integrating information from different views. It is widely applied in areas such as image recognition and bioinformatics, particularly demonstrating significant advantages when dealing with complex multimodal data.