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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Vydané v:IEEE transactions on knowledge and data engineering Ročník 31; číslo 10; s. 1863 - 1883
Hlavní autori: Li, Yingming, Yang, Ming, Zhang, Zhongfei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2018.2872063