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High-order relation driven multi-view representation learning
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DOI：10.1016/j.knosys.2026.116028.png)
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Abstract
En 中文
Existing multi-view feature fusion methods exhibit limitations in modeling high-order correlations, particularly the underutilization of matrix-structured representations, ineffective redundancy elimination, and insufficient deep model integration. To address these critical limitations, we propose a high-order relation-driven framework for interpretable multi-view feature fusion. To obtain accurate per-view representations, we first distill latent embeddings in a pair of complementary graph-structured spaces. These embeddings are then stacked into a third-order tensor, thereby enabling a unified characterization of global cross-view high-order interactions. Redundancy across tensor slices is subsequently reduced by enforcing a low-rank structure under the tensor-SVD nuclear norm, preserving discriminative information while eliminating duplicate components. Finally, feature separability is enhanced via a discriminative loss that explicitly enforces view-specific uniqueness constraints. Extensive experiments on eleven public datasets establish state-of-the-art multi-view clustering and classification performance, surpassing thirteen representative baselines. The resultant interpretable network, entirely free of parameter tuning, further enhances model transparency. The code is available at https://github.com/Elena365-star/HoRM .
Keywords:
multi-view feature fusion
high-order correlations
tensor-structured representations
redundancy elimination
discriminative loss
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