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Multi-view clustering based on view-attention driven
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DOI：10.1007/s13042-023-01787-9.png)
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Multi-view Clustering focuses on discovering coherence information and complementary information about the data among the different views, but often the views are accompanied by related information that is unrelated to the clustering goal. To address this problem, this paper proposes a clustering method named Multi-view Clustering Based on View-Attention Driven. Based on Autoencoder, the method learns feature representations from different views data using contrast learning and attention mechanisms. During the process of learning feature representations of different views, consider information of interest to other views. Guiding information is provided in an attention-driven manner to guide feature learning. On the one hand, it strengthens the focus on information in all views. On the other hand, it reduces the impact on information contained only in a subset of views that isn't relevant to clustering. In addition, random initialization is used to train the autoencoder, minimizing the network structure's influence on initialization parameters. Four challenging datasets were used to test the method and it was shown to outperform other competitive multi-view clustering methods.
Keyword:
Multi-view clustering
Attention mechanism
Contrast learning
Autoencoder
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3.2K
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