A Survey of Multi-View Representation 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...
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| Published in: | IEEE transactions on knowledge and data engineering Vol. 31; no. 10; pp. 1863 - 1883 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
IEEE
01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1041-4347, 1558-2191 |
| Online Access: | Get full text |
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| Abstract | 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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| AbstractList | 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. |
| Author | Yang, Ming Li, Yingming Zhang, Zhongfei |
| Author_xml | – sequence: 1 givenname: Yingming orcidid: 0000-0001-6859-9038 surname: Li fullname: Li, Yingming email: yingming@zju.edu.cn organization: College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China – sequence: 2 givenname: Ming surname: Yang fullname: Yang, Ming email: cauchym@zju.edu.cn organization: College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China – sequence: 3 givenname: Zhongfei surname: Zhang fullname: Zhang, Zhongfei email: zhongfei@zju.edu.cn organization: College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China |
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| SubjectTerms | Alignment Artificial neural networks canonical correlation analysis Correlation Correlation analysis Data mining Data models Kernel Learning systems Machine learning Markov processes Markov random fields multi-view deep learning Multi-view representation learning Neural networks Recurrent neural networks Representations |
| Title | A Survey of Multi-View Representation Learning |
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