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Anchor-Based Multiview Subspace Clustering With Anchor-wise and Class-wise Alignments

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This approach analyzes clustering results in ten datasets by addressing alignments, suggesting better accuracy in clustering outcomes.

The AMCA2 method improves clustering accuracy by addressing anchorwise and classwise alignments, resulting in better performance.
Experiments show that AMCA2 outperforms state-of-the-art clustering methods across ten benchmark datasets.
This method utilizes the Hadamard product for aligning multiple anchor graphs, enhancing computational efficiency.
The novel kernel anchor selection technique ensures the selection of more representative anchors, improving clustering results.

Liu et al. (2025) studied this question.

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