Abstract
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.
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The authors declare that they have no conflict of interest. This article does not contain any studies involving human participants or animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.
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Zhao, M., Yang, W., Nie, F. (2023). Transformer-Based Contrastive Multi-view Clustering via Ensembles. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14169. Springer, Cham. https://doi.org/10.1007/978-3-031-43412-9_40
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