| Citation: | CHEN M，MA X Y，ZHANG C，et al. Adaptive incomplete multi-view clustering[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（4）：1059-1073 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0178
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A high-quality complete initial graph can effectively improve the performance of incomplete multi-view clustering. However, inappropriate filling of missing values will lead to the initial graph losing the underlying structure of the data, and incomplete fusion of affine graphs of each view will make the unified learned representations miss the complementary information among the views. To address the aforementioned problems, an adaptive incomplete multi-view clustering (AIM) method was proposed in this paper. In the initial graph construction, AIM used the average value of similarity of valid views to fill the missing values at corresponding positions to obtain a complete potential structure of the data and introduced sparsity constraints to improve the robustness of the model to noise. In the graph optimization process, initially, low-rank constraints were introduced to capture the global structure of the data, followed by spectral constraints to enhance the closeness between data within classes to make the affine graph have a clearer block diagonal structure. The consistency constraints were introduced to minimize the differences between the affine graph and the unified representation of each view to capture the complementary information between the views. Ultimately, a unified robust representation graph with high discriminative features was obtained. The experimental comparisons with nine kinds of incomplete multi-view clustering in real and incomplete multi-view datasets simulated under multiple missing rates demonstrate that AIM obtains the best clustering performance.
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