Incomplete multi-view clustering which aims to solve the difficult clustering challenge on incomplete multi-view data collected from diverse domains with missing views has drawn considerable attention in recent years. In this paper, we propose a novel method, called consensus guided incomplete multi-view spectral clustering (CGIMVSC), to address the incomplete clustering problem. Specifically, CGIMVSC seeks to explore the local information within every single-view and the semantic consistent information shared by all views in a unified framework simultaneously, where the local structure is adaptively obtained from the incomplete data rather than pre-constructed via a k-nearest neighbor approach in the existing methods. Considering the semantic consistency of multiple views, CGIMVSC introduces a co-regularization constraint to minimize the disagreement between the common representation and the individual representations with respect to different views, such that all views will obtain a consensus clustering result. Experimental comparisons with some state-of-the-art methods on seven datasets validate the effectiveness of the proposed method on incomplete multi-view clustering.

旨在解决从缺少视角的不同领域收集的不完整多视图数据的困难聚类挑战的不完整多视图聚类近年来引起了相当大的关注。在本文中，我们提出了一种新的方法，称为共识制导的不完整多视图谱聚类（CGIMVSC），以解决不完整的聚类问题。具体而言，CMIGVSC寻求在统一框架中同时探索每个单视图内的局部信息和所有视图共享的语义一致信息，其中局部结构是从不完整数据中自适应获得的，而不是通过k近邻预先构造的。现有方法中的邻居方法。考虑到多个视图的语义一致性，CGIMVSC引入了一个共正则化约束，以最大程度地减少相同视图对不同视图的共同表示和单个表示之间的分歧，从而使所有视图都将获得共识聚类结果。通过对七个数据集的一些最新方法进行的实验比较，验证了该方法在不完整多视图聚类中的有效性。