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Structure-guided deep multi-view clustering
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DOI：10.1016/j.inffus.2025.103461.png)
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Abstract
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• Introduces a structure-guided deep multi-view clustering model to better exploit multi-view structural information. • Designs a cross-view consistency-based neighbor selection strategy to dynamically redefine positive pairs. • Employs a Gaussian distribution model to uncover latent structural relationships.
Keywords:
Multi-view clustering
Deep learning
Contrastive learning
Representation alignment
Journal
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15.5
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4.3K
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2.7W
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Cited Papers
Graph Regularized and Feature Aware Matrix Factorization for Robust Incomplete Multi-View Clustering
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