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:在数据挖掘应用中,通常会遇到多视角数据。从多视角数据中有效提取信息需要具体设计集成方法,以迎合具有多种观点的数据,这是非三重性和挑战性的。在本文件中,我们提出一种新的一步多视角集群方法,利用不同观点的共同和具体信息的双重代表性,其动机来自多视角数据不仅包含各种观点之间的一致知识,而且包含每种观点的独特知识。与此同时,为使代表学习更具体地与组合任务相关,建议了一个一步骤学习框架,将代表学习和分组分割作为一个整体结合起来。在此框架内,代表学习和分组分割相互受益,从而有效地改善组合业绩。在基准多视角数据集上进行的广泛实验的结果明确显示了拟议方法的优越性。