| الملخص: | With the rapid advancement of multimedia and data collection technologies, multi-view data is becoming increasingly prevalent. Unlike single-view data, multi-view data offers richer descriptive information and enhances the efficiency of structural information mining. In response to the multi-view clustering challenge, this study proposes a multi-view subspace clustering algorithm based on dual cross-view correlation detection. Considering the effects of noise disturbance and high-dimensional data redundancy on multi-view clustering, the proposed algorithm employs linear projection transformation to derive a potential low-redundancy representation of the original data. The accurate view-specific subspace representation is learned from the latent feature representation based on the self-representation property. To fully leverage the complementary information present in multi-view data, the proposed algorithm simultaneously detects cross-view correlations in both feature and subspace representations. Specifically, latent features are treated as low-level representations, enabling their diversity to be explored and retained by the Hilbert-Schmidt Independence Criterion (HSIC). For high-level clustering structures, the proposed algorithm ensures consistency among multi-view subspace representations by imposing a low-rank tensor constraint, which facilitates the exploration of high-order correlations and complementary information. The study employs an alternating direction minimization strategy with an augmented Lagrange multiplier to address the optimization problem. Experimental results on real datasets demonstrate that the proposed algorithm significantly outperforms suboptimal methods, achieving improvements in clustering accuracy of 3.00, 3.60, 1.90, 2.00, 7.50, and 1.90 percentage points across six benchmark datasets, respectively. These results validate the superiority and effectiveness of the algorithm.
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