Multi-level contrastive learning with graph convolutional network for multi-view clustering

doi:10.1016/j.eswa.2025.130573

Multi-level contrastive learning with graph convolutional network for multi-view clustering

2025-11-25

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DOI：10.1016/j.eswa.2025.130573

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• This paper presents a novel framework that integrates GCNs, autoencoders, and contrastive learning for MVC. • Captures high-order sample relationships via graph topology learning. • Dual-level contrast enhances discriminability and cross-view consistency. • Attention-based fusion dynamically weights multi-view features.

Multi-level contrastive learning with graph convolutional network for multi-view clustering

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