Most multi-view clustering algorithms apply to data with complete instances and clusters in the views. Recently, multi-view clustering on data with partial instances has been studied. In this paper, we study the more general version of the problem, i.e., multi-view clustering on data with partial instances and clusters in the views. We propose a non-negative matrix factorization (NMF) based algorithm. For the special case with partial instances, it introduces an instance-view-indicator matrix to indicate whether an instance exists in a view. Then, it maps the instances representing the same object to the same vector, and maps the instances representing different objects to different vectors. For the general case with partial instances and clusters, it further introduces a cluster-view-indicator matrix to indicate whether a cluster exists in a view. In each view, it also maps the instances representing the same object to the same vector, but it further makes the elements of the vector 0 if the elements correspond to missing clusters. Then it minimizes the disagreements between the approximated indicator vectors of instances representing the same object. Experimental results show that the proposed algorithm performs well on data with partial instances and clusters, and outperforms existing algorithms on data with partial instances.

大多数多视图群集算法适用于视图中具有完整实例和群集的数据。最近，已经研究了具有部分实例的数据的多视图聚类。在本文中，我们研究了该问题的更一般版本，即对具有部分实例和视图聚类的数据进行多视图聚类。我们提出了一种基于非负矩阵分解（NMF）的算法。对于具有部分实例的特殊情况，它引入了instance-view-indicator矩阵来指示视图中是否存在实例。然后，它将代表同一对象的实例映射到相同的向量，并将代表不同对象的实例映射到不同的向量。对于具有部分实例和群集的一般情况，它进一步引入了群集视图指示器矩阵以指示视图中是否存在群集。在每个视图中，它还将表示同一对象的实例映射到相同的向量，但是如果元素对应于丢失的群集，则进一步使向量的元素为0。然后，它最小化了表示同一对象的实例的近似指示符向量之间的分歧。实验结果表明，该算法在具有部分实例和聚类的数据上表现良好，并且优于具有部分实例和数据的现有算法。