提出高效旋转不变嵌入方法，解决多视角聚类中缺失数据问题。
Highly Efficient Rotation-Invariant Spectral Embedding for Scalable Incomplete Multi-View Clustering
- 从不完整双图学习视角特异性嵌入，融合互补信息。
- 统一模型恢复具备二阶旋转不变性的完整共识表示。
- 线性复杂度算法实现高效可扩展，适合大规模数据。
不完整多视角聚类因视图缺失面临严峻挑战。现有基于图的方法虽能恢复缺失实例或补全相似度矩阵，但仍存在三方面局限：(1) 恢复数据可能不适用于谱聚类，因忽略谱分析指导；(2) 复杂优化过程计算开销大，难以扩展至大规模场景；(3) 多数方法未处理谱嵌入中的旋转失配问题。为此，本文提出高效旋转不变谱嵌入（RISE）方法，用于可扩展的不完整多视角聚类。RISE从不完整二分图中学习视图特定嵌入以捕捉互补信息，同时在统一模型中恢复具有二阶旋转不变性的完整共识表示。此外，设计了具有线性复杂度和良好收敛性的快速交替优化算法求解该公式。在多个数据集上的大量实验表明，相较于最先进方法，RISE在有效性、可扩展性和效率方面均表现优异。
原文摘要 · Abstract (English)
Incomplete multi-view clustering presents significant challenges due to missing views. Although many existing graph-based methods aim to recover missing instances or complete similarity matrices with promising results, they still face several limitations: (1) Recovered data may be unsuitable for spectral clustering, as these methods often ignore guidance from spectral analysis; (2) Complex optimization processes require high computational burden, hindering scalability to large-scale problems; (3) Most methods do not address the rotational mismatch problem in spectral embeddings. To address these issues, we propose a highly efficient rotation-invariant spectral embedding (RISE) method for scalable incomplete multi-view clustering. RISE learns view-specific embeddings from incomplete bipartite graphs to capture the complementary information. Meanwhile, a complete consensus representation with second-order rotation-invariant property is recovered from these incomplete embeddings in a unified model. Moreover, we design a fast alternating optimization algorithm with linear complexity and promising convergence to solve the proposed formulation. Extensive experiments on multiple datasets demonstrate the effectiveness, scalability, and efficiency of RISE compared to the state-of-the-art methods.
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