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<p>Multi-view clustering, which integrates complementary information from heterogeneous data sources, has witnessed substantial progress in graph-based methodologies. However, prevalent graph-based methods often suffer from sparse initial graph constructions and high computational demands for large-scale data. Furthermore, effectively learning consistent underlying structures while mitigating noise and view discrepancies remains challenging, frequently leading to fusion distortion. In this paper, we present a novel Anchor-based Tensor Fusion method via High-Order Relations (ATFMH) to address these limitations, ATFMH leverages anchor graphs to build <i>high-order graphs</i>, systematically mitigating initial sparsity and capturing deeper structural relationships. Furthermore, ATFMH employs a <i>low-rank tensor nuclear norm constraint</i> on the jointly learned similarity graphs. This constraint promotes a consistent shared subspace across views, enhancing robustness against view-specific variations and noise. Extensive experiments demonstrate that ATFMH achieves an optimal balance between efficiency and accuracy. ATFMH significantly reduces computational and spatial costs while maintaining or exceeding state-of-the-art clustering performance across multiple benchmarks. This work provides an efficient and robust solution for large-scale multi-view data analysis.</p>

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Anchor-based tensor clustering of multi-view data through high order relational fusion

  • 期刊论文

  • 发表日期：

    2026年3月12日

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  • Yan Gong,
  • Tao Yang,
  • Yanying Mei,
  • Yanhua Shao

摘要

Multi-view clustering, which integrates complementary information from heterogeneous data sources, has witnessed substantial progress in graph-based methodologies. However, prevalent graph-based methods often suffer from sparse initial graph constructions and high computational demands for large-scale data. Furthermore, effectively learning consistent underlying structures while mitigating noise and view discrepancies remains challenging, frequently leading to fusion distortion. In this paper, we present a novel Anchor-based Tensor Fusion method via High-Order Relations (ATFMH) to address these limitations, ATFMH leverages anchor graphs to build high-order graphs, systematically mitigating initial sparsity and capturing deeper structural relationships. Furthermore, ATFMH employs a low-rank tensor nuclear norm constraint on the jointly learned similarity graphs. This constraint promotes a consistent shared subspace across views, enhancing robustness against view-specific variations and noise. Extensive experiments demonstrate that ATFMH achieves an optimal balance between efficiency and accuracy. ATFMH significantly reduces computational and spatial costs while maintaining or exceeding state-of-the-art clustering performance across multiple benchmarks. This work provides an efficient and robust solution for large-scale multi-view data analysis.