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• W4386071520 abstract "Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent years. However, most existing deep clustering methods learn consensus representation or view-specific representations from multiple views via view-wise aggregation way, where they ignore structure relationship of all samples. In this paper, we propose a novel multi-view clustering network to address these problems, called Global and Cross-view Feature Aggregation for Multi-View Clustering (GCFAggMVC). Specifically, the consensus data presentation from multiple views is obtained via cross-sample and cross-view feature aggregation, which fully explores the complementary of similar samples. Moreover, we align the consensus representation and the view-specific representation by the structure-guided contrastive learning module, which makes the view-specific representations from different samples with high structure relationship similar. The proposed module is a flexible multi-view data representation module, which can be also embedded to the incomplete multi-view data clustering task via plugging our module into other frameworks. Extensive experiments show that the proposed method achieves excellent performance in both complete multi-view data clustering tasks and incomplete multi-view data clustering tasks." @default.
• W4386071520 created "2023-08-23" @default.
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• W4386071520 date "2023-06-01" @default.
• W4386071520 modified "2023-10-18" @default.
• W4386071520 title "GCFAgg: Global and Cross-View Feature Aggregation for Multi-View Clustering" @default.
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• W4386071520 doi "https://doi.org/10.1109/cvpr52729.2023.01902" @default.
• W4386071520 hasPublicationYear "2023" @default.
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