Consistent graph embedding network with optimal transport for incomplete multi-view clustering

Existing incomplete multi-view learning models focus on reconstructing the latent variables of multiple views by exploring complementary and consistent information among diverse views. However, filling the missing information for views results in a loss of consistency, while fusion and reconstruction between views face over-fitting problems. The optimal transport algorithm delicately measures the distance of two distributions, resulting in decreased reconstruction errors and guaranteeing consistency and complementarity across multiple views of the data. In light of this, this paper proposes the incorporation of the optimal transport algorithm into the framework of incomplete multi-view clustering. The proposed consistent graph embedding network (CGEN-OT) via optimal transport combines the adversarial module and fusion module to learn a completed latent graph embedding. Specifically, CGEN-OT utilizes an adversarial module to generate complete views and fuses them into a consistent embedding, and introduces reconstruction loss and Sinkhorn loss to jointly optimize the proposed network and obtain superior latent graph embedding and clustering performance. To further validate the clustering accuracy and convergence of the CGEN-OT, experimental evaluation was conducted on six distinct incomplete datasets. A comparison with existing state-of-the-art models highlights the superiority of the proposed framework.