期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 31, 期 10, 页码 1863-1883出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2018.2872063
关键词
Multi-view representation learning; canonical correlation analysis; multi-view deep learning
类别
资金
- National Natural Science Foundation of China [61702448, 61672456]
- Fundamental Research Funds for the Central Universities [2017QNA5008, 2017FZA5007]
- Zhejiang UniversityHIKVision Joint lab
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.
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