通过联合低秩约束提升多视图聚类的完整相似度张量重建能力。
Joint Tensor and Inter-View Low-Rank Recovery for Incomplete Multiview Clustering
- 融合视内与视间低秩信息，联合优化相似度张量与表示恢复。
- 在合成与真实数据集上显著提升聚类准确率和鲁棒性。
- 适合处理存在缺失样本的多视图聚类任务，尤其适用于高噪声场景。
不完整多视图聚类（IMVC）因其在真实多视图聚类应用中有效应对各视图缺失样本问题而受到广泛关注。现有方法通常通过学习可用视图的一致表示或利用底层流形结构重构缺失样本。然而，先前研究在重构相似度图张量时仅利用低管秩信息，忽略了视间相关性的挖掘。本文提出一种新的联合张量与视间低秩恢复（JTIV-LRR）方法，将IMVC建模为联合优化问题，同时整合不完整相似度图学习与张量表示恢复。通过利用视内与视间低秩特性，结合稀疏噪声去除及不同模式下的低管秩约束，实现对完整相似度张量的稳健估计。在合成与真实数据集上的大量实验表明，该方法相比当前最优方法在聚类准确率和鲁棒性方面均有显著提升。
原文摘要 · Abstract (English)
Incomplete multiview clustering (IMVC) has gained significant attention for its effectiveness in handling missing sample challenges across various views in real-world multiview clustering applications. Most IMVC approaches tackle this problem by either learning consensus representations from available views or reconstructing missing samples using the underlying manifold structure. However, the reconstruction of learned similarity graph tensor in prior studies only exploits the low-tubal-rank information, neglecting the exploration of inter-view correlations. This paper propose a novel joint tensor and inter-view low-rank Recovery (JTIV-LRR), framing IMVC as a joint optimization problem that integrates incomplete similarity graph learning and tensor representation recovery. By leveraging both intra-view and inter-view low rank information, the method achieves robust estimation of the complete similarity graph tensor through sparse noise removal and low-tubal-rank constraints along different modes. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of the proposed approach, achieving significant improvements in clustering accuracy and robustness compared to state-of-the-art methods.
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