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Adaptive Hypergraph-Induced Dual-Structure Tensor Embedding for Multi-View Image Representation Learning
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DOI：10.1109/tip.2026.3736794.png)
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Abstract
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Multi-View Multi-Label Learning is pivotal for visual content understanding yet faces two fundamental bottlenecks: (i) Structural Decoupling, where prevalent matrix flattening severs intrinsic high-order correlations between visual features and semantic concepts; and (ii) Semantic Uniformity Bias, where the neglect of label topology heterogeneity leads to overfitting on ambiguous annotations. To address these, we propose the Adaptive HypergrAph-Induced Dual-Structure Tensor Embedding (AHATE) framework. Drawing conceptual inspiration from tensor spectral graph theory, we introduce a novel Dual-Structure Tensor Nuclear Norm (DS-TNN). Unlike loose additive regularization, this mechanism functions as a high-order spectral filter in the Fourier domain, explicitly coupling feature manifolds with label topology to rigorously enforce consensus while suppressing structural noise. Furthermore, grounded in network centrality theory, we design an Adaptive Hypergraph-Weighted Loss. By leveraging PageRank to quantify topological importance, this strategy prioritizes high-confidence structural anchors over ambiguous instances. Finally, a joint sparse projection mechanism is incorporated to eliminate redundancy. Extensive experiments demonstrate that AHATE achieves statistically significant superiority. Specifically, compared to the second-best methods, it improves Average Precision by up to 5.52% and reduces Hamming Loss by up to 4.40%, while achieving the lowest Hamming Loss of 0.0137 on Corel5k. The full source code, including preprocessing and configuration scripts, is available at https://github.com/hpinty/AHATE.git
Keywords:
Multi-view learning
image representation learning
tensor embedding
hypergraph
multi-label classification
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