DealMVC: Dual Contrastive Calibration for Multi-view Clustering
- URL: http://arxiv.org/abs/2308.09000v3
- Date: Tue, 7 Nov 2023 02:38:17 GMT
- Title: DealMVC: Dual Contrastive Calibration for Multi-view Clustering
- Authors: Xihong Yang, Jiaqi Jin, Siwei Wang, Ke Liang, Yue Liu, Yi Wen, Suyuan
Liu, Sihang Zhou, Xinwang Liu, En Zhu
- Abstract summary: We propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC)
We first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph.
During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels.
- Score: 78.54355167448614
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Benefiting from the strong view-consistent information mining capacity,
multi-view contrastive clustering has attracted plenty of attention in recent
years. However, we observe the following drawback, which limits the clustering
performance from further improvement. The existing multi-view models mainly
focus on the consistency of the same samples in different views while ignoring
the circumstance of similar but different samples in cross-view scenarios. To
solve this problem, we propose a novel Dual contrastive calibration network for
Multi-View Clustering (DealMVC). Specifically, we first design a fusion
mechanism to obtain a global cross-view feature. Then, a global contrastive
calibration loss is proposed by aligning the view feature similarity graph and
the high-confidence pseudo-label graph. Moreover, to utilize the diversity of
multi-view information, we propose a local contrastive calibration loss to
constrain the consistency of pair-wise view features. The feature structure is
regularized by reliable class information, thus guaranteeing similar samples
have similar features in different views. During the training procedure, the
interacted cross-view feature is jointly optimized at both local and global
levels. In comparison with other state-of-the-art approaches, the comprehensive
experimental results obtained from eight benchmark datasets provide substantial
validation of the effectiveness and superiority of our algorithm. We release
the code of DealMVC at https://github.com/xihongyang1999/DealMVC on GitHub.
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