Multi-SSALvcAE: Self-Supervised Adversarial Learning-Based View-Common Latent AutoEncoders for Multiview Clustering

Multiview clustering (MVC) is a fundamental research topic in the field of machine learning and data mining, which has been developed rapidly and made significant progress recently. However, the current works tend to learn the individual representation of each view and then naviely merge or align them to achieve a shared representation of multiview data. By doing this, they often ignore the interference caused by the entanglement among multiple views, leading to the shared latent embedding cannot well model the correlation of all views. To this end, in this article, we propose a novel self-supervised adversarial learning-based view-common latent autoencoders for MVC, termed by multi-SSALvcAE. Specifically, the proposed method can effectively disentangle the unique and common information of each view by virtue of multiview adversarial latent autoencoders. And then only the common parts are fused to form the shared information, after being aligned deliberately on multiview semantic space. Experimental results show that our method achieved the promising results on several datasets, against the state-of-the-arts.