Multi-view learning is a machine learning paradigm designed to process and analyze data that comes from multiple sources or perspectives, enhancing the performance of learning tasks by leveraging the complementary information from these different views. This approach is particularly advantageous in scenarios where single-view data might be incomplete or insufficient, as it aims to improve generalization and robustness by combining multiple datasets into a unified model.