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arXiv · 2302.13339

MCoCo: Multi-level Consistency Collaborative Multi-view Clustering

Yiyang Zhou·Qinghai Zheng·Wenbiao Yan·Yifei Wang·Pengcheng Shi·Jihua Zhu
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Abstract

Multi-view clustering can explore consistent information from different views to guide clustering. Most existing works focus on pursuing shallow consistency in the feature space and integrating the information of multiple views into a unified representation for clustering. These methods did not fully consider and explore the consistency in the semantic space. To address this issue, we proposed a novel Multi-level Consistency Collaborative learning framework (MCoCo) for multi-view clustering. Specifically, MCoCo jointly learns cluster assignments of multiple views in feature space and aligns semantic labels of different views in semantic space by contrastive learning. Further, we designed a multi-level consistency collaboration strategy, which utilizes the consistent information of semantic space as a self-supervised signal to collaborate with the cluster assignments in feature space. Thus, different levels of spaces collaborate with each other while achieving their own consistency goals, which makes MCoCo fully mine the consistent information of different views without fusion. Compared with state-of-the-art methods, extensive experiments demonstrate the effectiveness and superiority of our method.

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Yiyang Zhou, Qinghai Zheng, Wenbiao Yan, Yifei Wang, Pengcheng Shi, Jihua Zhu. 2023-02-26. MCoCo: Multi-level Consistency Collaborative Multi-view Clustering. https://arxiv.org/abs/2302.13339

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