This paper provides a comprehensive survey of multi-view learning, a paradigm that leverages multiple views of the same data to improve learning performance. The authors classify multi-view learning algorithms into three main groups: co-training, multiple kernel learning, and subspace learning. Co-training algorithms train alternately to maximize mutual agreement on distinct views, while multiple kernel learning combines kernels to exploit different notations of similarity. Subspace learning aims to find a shared latent subspace among multiple views. The paper also discusses the principles underlying multi-view learning, including the consensus and complementary principles, and explores methods for constructing and evaluating multiple views. Additionally, it reviews various techniques for combining multiple views, such as linear and nonlinear kernel combinations, and highlights the importance of considering the relationships between different views to enhance the effectiveness of multi-view learning.This paper provides a comprehensive survey of multi-view learning, a paradigm that leverages multiple views of the same data to improve learning performance. The authors classify multi-view learning algorithms into three main groups: co-training, multiple kernel learning, and subspace learning. Co-training algorithms train alternately to maximize mutual agreement on distinct views, while multiple kernel learning combines kernels to exploit different notations of similarity. Subspace learning aims to find a shared latent subspace among multiple views. The paper also discusses the principles underlying multi-view learning, including the consensus and complementary principles, and explores methods for constructing and evaluating multiple views. Additionally, it reviews various techniques for combining multiple views, such as linear and nonlinear kernel combinations, and highlights the importance of considering the relationships between different views to enhance the effectiveness of multi-view learning.