  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.
