1 INTRODUCTION
1.1 Main Differences from Other Related Surveys
1.2 Challenges for Multi-View Representation Learning
2 A TAXONOMY ON MULTI-VIEW REPRESENTATION LEARNING
2.1 Multi-View Representation Alignment
2.2 Multi-View Representation Fusion
3 MULTI-VIEW REPRESENTATION ALIGNMENT
3.1 Correlation-based Alignment
3.2 Distance and Similarity-based Alignment
4 MULTI-VIEW REPRESENTATION FUSION
4.1 Graphical Model-based Representation Fusion
4.2 Neural Network-based Representation Fusion
5 APPLICATIONS
5.1 Cross-media Retrieval
5.2 Natural Language Processing
5.3 Video Analysis
5.4 Recommender System
6 CONCLUSION