Multi-view data are commonly encountered in data mining applications. Effective extraction of information from multi-view data requires specific design of clustering methods to cater for data with multiple views, which is non-trivial and challenging. In this paper, we propose a novel one-step multi-view clustering method by exploiting the dual representation of both the common and specific information of different views. The motivation originates from the rationale that multi-view data contain not only the consistent knowledge between views but also the unique knowledge of each view. Meanwhile, to make the representation learning more specific to the clustering task, a one-step learning framework is proposed to integrate representation learning and clustering partition as a whole. With this framework, the representation learning and clustering partition mutually benefit each other, which effectively improve the clustering performance. Results from extensive experiments conducted on benchmark multi-view datasets clearly demonstrate the superiority of the proposed method.

翻译：多视图数据在数据挖掘应用中普遍存在。有效提取多视图数据中的信息需要专门设计聚类方法以适配具有多个视图的数据，这是一项具有挑战性的非平凡任务。本文提出一种新颖的单步多视图聚类方法，通过利用不同视图的共有信息和特有信息的双表示来解决问题。其动机源于多视图数据不仅包含视图间的一致性知识，也包含各视图的独有知识这一原理。同时，为使表示学习更贴合聚类任务，我们提出一种将表示学习与聚类划分整合为一体的单步学习框架。在该框架下，表示学习与聚类划分相互促进，有效提升了聚类性能。在基准多视图数据集上进行的大量实验结果表明，所提方法具有显著优越性。