Deep multi-view fuzzy consensus with uncertainty
DOI:
https://doi.org/10.54939/1859-1043.j.mst.CSCE9.2025.83-91Keywords:
Multi-view clustering; Fuzzy clustering; Consensus embedding; Uncertainty modeling; Deep autoencoder; Entropy regularization.Abstract
Clustering multi-view data is challenging due to feature heterogeneity, inter-view inconsistency, and inherent uncertainty. Traditional fuzzy clustering methods (FCM, PFCM) cannot exploit complementary information, while most multi-view models overlook uncertainty and adaptive weighting. We propose a unified deep fuzzy framework named DMFCU (Deep Multi-view Fuzzy Consensus with Uncertainty), which integrates multi-view autoencoder reconstruction, fuzzy clustering in a consensus space, cross-view alignment, uncertainty regularization, and entropy-based view weighting. The optimization alternates updates of memberships, centroids, and view weights with backpropagation for representation learning. Experiments on benchmark datasets show that DMFCU consistently outperforms state-of-the-art fuzzy clustering approaches in accuracy, NMI, and robustness under noisy or incomplete views. The framework achieves strong modeling capacity with comparable complexity, offering a principled solution for reliable multi-view clustering under uncertainty.
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