Adversarial correlated autoencoder for unsupervised multi-view representation learning

摘要

To eliminate the view discrepancy of multi-view data due to different distributions, the key is to learn the common representation for multi-view data in many practical applications. To achieve the end, we propose a novel unsupervised multi-view representation learning method (called Adversarial Correlated AutoEncoder, AdvCAE). In brief, AdvCAE utilizes a deep structure to achieve nonlinear representation and adversarial learning scheme for distribution matching. To be specific, AdvCAE performs like an adversarial autoencoder (AAE) which could conduct variational inference by matching the aggregated posteriors of the latent variable with a specific prior distribution. Benefiting from our model, the representations of different views could follow the same distribution, thus learning the common representation for different views. To the best of our knowledge, AdvCAE could be one of the first unsupervised multi-view representation learning approaches that work in the manner of adversarial learning. To verify the effectiveness of the proposed method, we conduct experiments on five public real-world datasets w.r.t. the applications of cross-view classification and cross-view retrieval tasks. The experimental results show that our method remarkably outperforms than 15 state-of-the-art methods.