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A novel framework called Adaptive Granular Hypergraph Generation (MGHRL) is introduced, which generates hyperedges at multiple levels of granularity through the adaptive splitting of granular-ball, effectively capturing high-order relationships based on the graph's topological structure.
This work proposes Selective Constraint Learning (SCL), a framework that introduces external semantic guidance as a stable prior for unsupervised cross-domain image retrieval and designs a generic constraint loss to jointly facilitate intra-domain compactness and inter-domain alignment.
The method generates granular balls in the current fused kernel space and alternates kernel-weight learning with granular-ball membership updates, allowing the representation to adapt to changes in the fused-kernel geometry.
Experimental results demonstrate that the proposed adaptive and efficient KNN approach via granular-ball computing outperforms existing KNN variants across multiple datasets in terms of both accuracy and efficiency.
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