GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering

• URL: http://arxiv.org/abs/2305.06799v1
• Date: Thu, 11 May 2023 13:41:13 GMT
• Title: GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering
• Authors: Weiqing Yan, Yuanyang Zhang, Chenlei Lv, Chang Tang, Guanghui Yue, Liang Liao, Weisi Lin
• Abstract summary: Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way. We propose a novel multi-view clustering network to address these problems, called Global and Cross-view Feature aggregation for Multi-View Clustering (GggMVC) We show that the proposed method achieves excellent performance in both complete multi-view data clustering tasks and incomplete multi-view data clustering tasks.
• Score: 45.530950521907265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 ofsimilar 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.

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