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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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 31; no. 10; pp. 1863 - 1883
Main Authors: Li, Yingming, Yang, Ming, Zhang, Zhongfei
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary: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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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2018.2872063