Transformer-Based Contrastive Multi-view Clustering via Ensembles

Multi-view spectral clustering has achieved considerable performance in practice because of its ability to explore nonlinear structure information. However, most existing methods belong to shallow models and are sensitive to the original similarity graphs. In this work, we proposed a novel model of Transformer-based contrastive multi-view clustering via ensembles (TCMCE) to solve the above issues. Our model integrates the self-attention mechanism, ensemble clustering, graph reconstruction, and contrastive learning into a unified framework. From the viewpoint of orthogonal and nonnegative graph reconstruction, TCMCE aims to learn a common spectral embedding as the indicator matrix. Then the graph contrastive learning is performed on the reconstructed graph based on the fusion graph via ensembles. Extensive experiments on six real-world datasets have verified the effectiveness of our model on multi-view clustering tasks compared with the state-of-the-art models.