Multi-View Representation Learning: A Survey from Shallow Methods to Deep Methods

Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas. This paper first reviews the root methods and theories on multi-view representation learning, especially on canonical correlation analysis (CCA) and its several extensions. And then we investigate the advancement of multi-view representation learning that ranges from shallow methods including multi-modal topic learning, multi-view sparse coding, and multi-view latent space Markov networks, to deep methods including multi-modal restricted Boltzmann machines, multi-modal autoencoders, and multi-modal recurrent neural networks. Further, we also provide an important perspective from manifold alignment for multi-view representation learning. Overall, this survey aims to provide an insightful overview of theoretical basis and current developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications.