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随着多视图学习的迅速发展，如何有效地整合来自不同视图的信息进行聚类分析，已成为当前学术界和工业界的重要研究课题，并推动了一系列高效方法的涌现。然而，现有的方法仍面临三大挑战。第一，现实数据往往具有不确定性和低判别性特征，因此直接从原始数据提取锚点图会导致性能欠佳；第二，现有方法大多假设视图间存在共性信息并依靠此类信息进行聚类，却忽视了各视图的特性信息；第三，如何进一步探索和利用所学锚点图来提升聚类性能仍是一个开放性的问题。对此，提出了一种新型双锚点图模糊聚类方法。针对前2个问题，设计基于矩阵分解的双锚点图学习框架，通过提取各视图的高判别性隐式表征，从中推导出共有锚点图和特定锚点图；针对第3个问题，开发具有协同学习机制的锚点图模糊聚类方法，构建双锚点图驱动的模糊隶属度结构保持机制以提升聚类质量。同时，还引入负香农熵实现视图权重的自适应调整。最后，本文在多个基准数据集上进行了广泛的实验验证。结果表明，提出的DAG＿FC方法在大多数指标和数据集上都展现出显著优势。特别是在Yale数据集上，DAG＿FC的NMI值相较于对比方法分别高出约30%和20%。此外，实验也证实基于锚点图的聚类方法在性能上普遍优于传统的基于子空间的聚类方法。通过引入潜在表征提取技术和设计专用的聚类算法，本文进一步提升了所提方法的聚类性能。
Abstract：In recent years, with the rapid development of multi-view learning, how to effectively integrate information from different views for clustering analysis has become an important research topic in both academia and industry,driving the emergence of numerous efficient methods. However, current methods still face three major challenges.First, suboptimal anchor graphs often result from the inherent uncertainty and low discriminability of real-world data.Second, prevalent approaches primarily focus on common information between views, overlooking valuable view-specific information. Third, effectively leveraging the learned anchor graph to improve clustering remains under-explored.To overcome these challenges, this paper proposes a novel dual anchor graph fuzzy clustering framework. To address the first two challenges, we design a matrix factorization-based dual anchor graph learning framework. This framework extracts discriminative hidden representations from each view and subsequently derives both common and specific anchor graphs. For the third challenge, we develop an anchor graph fuzzy clustering method with a cooperative learning mechanism. This method constructs a dual anchor graph-driven fuzzy membership structure preservation mechanism to enhance clustering quality. Additionally, we introduce negative Shannon entropy to achieve adaptive view weighting. Extensive experiments on multiple benchmark datasets validate the effectiveness of the proposed DAG＿FC method. The results show that DAG＿FC outperforms competing methods on most metrics and datasets,achieving NMI improvements of approximately 30% and 20% over comparative methods on the Yale dataset. Moreover, the experiments also confirm that anchor graph-based clustering methods generally perform better than traditional subspace-based clustering methods. By incorporating hidden representation extraction techniques and designing specialized clustering algorithms, this paper further enhances the clustering performance of the proposed method.
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基本信息:
DOI：10.12194/j.ntu.20250610001
中图分类号:TP391.41
引用信息:
[1]朱成豪,丁卫平,张炜.基于双锚点图的多视图模糊聚类[J].南通大学学报(自然科学版),2025,24(03):64-74.DOI:10.12194/j.ntu.20250610001.
基金信息: