Learning Representation Of Multi-View Data

Multi-view data representation learning refers to the extraction of a unified and representative feature representation from data obtained from multiple different perspectives. Its goal is to enhance the robustness and generalization ability of data representations by integrating multi-source information, thereby achieving more accurate recognition and classification in complex data environments. This method has significant application value in fields such as computer vision, natural language processing, and bioinformatics, effectively improving model performance and data utilization efficiency.