Multiview clustering (MVC) collects complementary and abundant information, which draws much attention in machine learning and data mining community. Existing MVC methods usually hold the assumption that all the views are complete. However, multiple source data are often incomplete in real‐world applications, and so on sensor failure or unfinished collection process, which gives rise to incomplete multiview clustering (IMVC). Although enormous efforts have been devoted in IMVC, there still are some urgent issues that need to be solved: (i) The locality among multiple views has not been utilized in the existing mechanism; (ii) Existing methods inappropriately force all the views to share consensus representation while ignoring specific structures. In this paper, we propose a novel method termed partial MVC with locality graph regularization to address these issues. First, followed the traditional IMVC approaches, we construct weighted semi‐nonnegative matrix factorization models to handle incomplete multiview data. Then, upon the consensus representation matrix, the locality graph is constructed for regularizing the shared feature matrix. Moreover, we add the coefficient regression term to constraint the various base matrices among views. We incorporate the three aforementioned processes into a unified framework, whereas they can negotiate with each other serving for learning tasks. An effective iterative algorithm is proposed to solve the resultant optimization problem with theoretically guaranteed convergence. The comprehensive experiment results on several benchmarks demonstrate the effectiveness of the proposed method.

中文翻译：

---

具有局部图正则化的部分多视图聚类

多视图聚类（MVC）收集补充和丰富的信息，这在机器学习和数据挖掘社区中引起了很多关注。现有的MVC方法通常假设所有视图都是完整的。但是，在实际应用中，多个源数据通常是不完整的，例如传感器故障或收集过程未完成，这会导致不完整的多视图聚类（IMVC）。尽管IMVC付出了巨大的努力，但仍然需要解决一些紧迫的问题：（i）现有机制未利用多视图之间的局部性；（ii）现有方法不适当地迫使所有观点共享共识表示，而忽略了特定结构。在本文中，我们提出了一种称为局部MVC的局部图正则化的新方法来解决这些问题。首先，遵循传统的IMVC方法，我们构造加权半负矩阵分解模型来处理不完整的多视图数据。然后，在共识表示矩阵上，构造局部图以对共享特征矩阵进行正则化。此外，我们添加了系数回归项以约束视图之间的各种基本矩阵。我们将上述三个过程合并到一个统一的框架中，而它们可以彼此协商以服务于学习任务。提出了一种有效的迭代算法，以理论上保证的收敛性来解决由此产生的优化问题。