Multiview clustering explores complementary information among distinct views to enhance clustering performance under the assumption that all samples have complete information in all available views. However, this assumption does not hold in many real applications, where the information of some samples in one or more views may be missing, leading to partial multiview clustering problems. In this case, significant performance degeneration is usually observed. A collection of partial multiview clustering algorithms has been proposed to address this issue and most treat all different views equally during clustering. In fact, because different views provide features collected from different angles/feature spaces, they might play different roles in the clustering process. With the diversity of different views considered, in this study, a novel adaptive method is proposed for partial multiview clustering by automatically adjusting the contributions of different views. The samples are divided into complete and incomplete sets, while a joint learning mechanism is established to facilitate the connection between them and thereby improve clustering performance. More specifically, the method is characterized by a joint optimization model comprising two terms. The first term mines the underlying cluster structure from both complete and incomplete samples by adaptively updating their importance in all available views. The second term is designed to group all data with the aid of the cluster structure modeled in the first term. These two terms seamlessly integrate the complementary information among multiple views and enhance the performance of partial multiview clustering. Experimental results on real-world datasets illustrate the effectiveness and efficiency of our proposed method.

中文翻译：

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适用于部分多视图聚类的自适应样本级图组合。

在所有样本在所有可用视图中均具有完整信息的假设下，多视图聚类在不同视图之间探索互补信息，以增强聚类性能。但是，这种假设在许多实际应用中并不成立，在一个实际应用中，一个或多个视图中某些样本的信息可能会丢失，从而导致部分多视图聚类问题。在这种情况下，通常会观察到明显的性能下降。已经提出了部分多视图聚类算法的集合来解决这个问题，并且在聚类中大多数都平等地对待所有不同的视图。实际上，由于不同的视图提供了从不同角度/特征空间收集的特征，因此它们在聚类过程中可能扮演不同的角色。考虑到不同观点的多样性，在这项研究中，通过自动调整不同视图的贡献，提出了一种用于局部多视图聚类的自适应方法。样本分为完整集和不完整集，同时建立了联合学习机制以促进它们之间的联系，从而提高聚类性能。更具体地说，该方法的特征在于包括两个项的联合优化模型。第一项通过在所有可用视图中自适应更新其重要性，从完整和不完整的样本中挖掘潜在的群集结构。第二项旨在借助第一项中建模的集群结构对所有数据进行分组。这两个术语无缝地整合了多个视图之间的补充信息，并增强了部分多视图集群的性能。