A Survey of Multi-View Representation Learning

作者：Li, Yingming^*; Yang, Ming; Zhang, Zhongfei

来源：IEEE Transactions on Knowledge and Data Engineering, 2019, 31(10): 1863-1883.

DOI：10.1109/TKDE.2018.2872063

• Multi-view representation learning
• canonical correlation analysis
• multi-view deep learning

Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas. This paper introduces two categories for multi-view representation learning: multi-view representation alignment and multi-view representation fusion. Consequently, we first review the representative methods and theories of multi-view representation learning based on the perspective of alignment, such as correlation-based alignment. Representative examples are canonical correlation analysis (CCA) and its several extensions. Then, from the perspective of representation fusion, we investigate the advancement of multi-view representation learning that ranges from generative methods including multi-modal topic learning, multi-view sparse coding, and multi-view latent space Markov networks, to neural network-based methods including multi-modal autoencoders, multi-view convolutional neural networks, and multi-modal recurrent neural networks. Further, we also investigate several important applications of multi-view representation learning. Overall, this survey aims to provide an insightful overview of theoretical foundation and state-of-the-art developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications.

• 出版日期2019-10
• 单位浙江大学