Abstract Multi-view clustering has received an increasing attention in many applications, where different views of objects can provide complementary information to each other. Existing approaches on multi-view clustering mainly focus on extending Non-negative Matrix Factorization (NMF) by enforcing the constraint over the coefficient matrices from different views in order to preserve their consensus. In this paper, we argue that it is more reasonable to utilize the high-level manifold consensus rather than the low-level coefficient matrix consensus (as conducted in state-of-the-art approaches) to better capture the underlying clustering structure of the data. For this purpose, we propose MMRSC (Multiple Manifold Regularized Sparse Coding), which aims to preserve the consensus over multiple manifold structures from different views. Experimental results on two publicly available real-world image datasets demonstrate that our proposed approach can significantly outperform the state-of-the-art approaches for the multi-view image clustering task. Moreover, we also conduct computational complexity analysis and the result shows that MMRSC can effective handle the multi-view clustering problem without increasing the computational cost as compared to GraphSC.

中文翻译：

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基于稀疏编码和流形共识的多视图图像聚类

摘要 多视图聚类在许多应用中受到越来越多的关注，其中对象的不同视图可以相互提供补充信息。现有的多视图聚类方法主要侧重于通过对来自不同视图的系数矩阵实施约束来扩展非负矩阵分解 (NMF)，以保持它们的一致性。在本文中，我们认为利用高级流形共识而不是低级系数矩阵共识（如在最先进的方法中进行的）来更好地捕捉潜在的聚类结构更合理。数据。为此，我们提出了 MMRSC（Multiple Manifold Regularized Sparse Coding），旨在从不同的角度保持对多个流形结构的共识。两个公开可用的真实世界图像数据集的实验结果表明，我们提出的方法可以显着优于多视图图像聚类任务的最新方法。此外，我们还进行了计算复杂度分析，结果表明，与 GraphSC 相比，MMRSC 可以有效地处理多视图聚类问题，而不会增加计算成本。