Most multi-view clustering methods assume that each view has complete instances and clusters. However, in real world applications, the instances or clusters may be missed in some views. Recently, multi-view clustering on data with partially mapped instances has been studied. In this paper, we study the multi-view clustering on data with partially mapped instances and clusters to extend the application of multi-view clustering. We propose a NMF (Non-negative Matrix Factorization) based algorithm which separately deals with the mapped clusters/instances and the individual clusters/instances, i.e., both the basis matrix and the indicator matrix consist of a mapped part and an individual part. By bounding the mapped instances to reduce to the same indicator vectors, the mapped instances and clusters connect multiple views and guide to find the indicator vectors of all the instances. Furthermore, we improve the algorithm by using locally geometrical information to reduce the negative impact caused by multi-view interaction. Experiments show that the proposed algorithms perform well on data with partially mapped instances and clusters.

大多数多视图群集方法都假定每个视图都有完整的实例和群集。但是，在现实世界的应用程序中，实例或群集在某些视图中可能会丢失。最近，已经研究了具有部分映射实例的数据的多视图聚类。在本文中，我们研究具有部分映射的实例和聚类的数据的多视图聚类，以扩展多视图聚类的应用。我们提出了一种基于NMF（非负矩阵分解）的算法，该算法分别处理映射的群集/实例和单个群集/实例，即基础矩阵和指标矩阵均由映射部分和单个部分组成。通过限制映射的实例以减少为相同的指标向量，映射的实例和群集连接多个视图，并引导查找所有实例的指标向量。此外，我们通过使用局部几何信息来改进算法，以减少多视图交互所造成的负面影响。实验表明，该算法对具有部分映射实例和聚类的数据表现良好。